Optimal Fast Control and Scheduling of Power Distribution System Using Integrated Receding Horizon Control and Convex Conic Programming
In this paper, a convex optimal power flow (OPF) formulation integrated within receding horizon control (RHC) architecture using second-order conic programming (SOCP) is proposed. The main advantages of the proposed method are 1) global optimal scheduling with faster computation time; 2) dynamic mod...
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| Veröffentlicht in: | IEEE transactions on industry applications 2016-05, Vol.52 (3), p.2596-2606 |
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| creator | Moghadasi, Seyedmahdi Kamalasadan, Sukumar |
| description | In this paper, a convex optimal power flow (OPF) formulation integrated within receding horizon control (RHC) architecture using second-order conic programming (SOCP) is proposed. The main advantages of the proposed method are 1) global optimal scheduling with faster computation time; 2) dynamic models with online control within optimization routine; and 3) integration of uncertain resources and measurements. The effectiveness of this method is evaluated using modified IEEE 32-bus and IEEE 119-bus distribution test systems considering network constraints such as energy market interactions, storage dynamics, and uncertain model of wind generation. The efficiency of the proposed method compared to RHC ac optimal power flow (RHC-ACOPF) is also evaluated using real-time simulator. The results show that the proposed method outperforms the RHC-ACOPF and guarantees global optimal solution. The proposed method also provides effective usage of energy storage system since dynamic modeling of energy storage within the optimization algorithm is possible using RHC integration. |
| doi_str_mv | 10.1109/TIA.2016.2531623 |
| format | Article |
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The main advantages of the proposed method are 1) global optimal scheduling with faster computation time; 2) dynamic models with online control within optimization routine; and 3) integration of uncertain resources and measurements. The effectiveness of this method is evaluated using modified IEEE 32-bus and IEEE 119-bus distribution test systems considering network constraints such as energy market interactions, storage dynamics, and uncertain model of wind generation. The efficiency of the proposed method compared to RHC ac optimal power flow (RHC-ACOPF) is also evaluated using real-time simulator. The results show that the proposed method outperforms the RHC-ACOPF and guarantees global optimal solution. 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The main advantages of the proposed method are 1) global optimal scheduling with faster computation time; 2) dynamic models with online control within optimization routine; and 3) integration of uncertain resources and measurements. The effectiveness of this method is evaluated using modified IEEE 32-bus and IEEE 119-bus distribution test systems considering network constraints such as energy market interactions, storage dynamics, and uncertain model of wind generation. The efficiency of the proposed method compared to RHC ac optimal power flow (RHC-ACOPF) is also evaluated using real-time simulator. The results show that the proposed method outperforms the RHC-ACOPF and guarantees global optimal solution. The proposed method also provides effective usage of energy storage system since dynamic modeling of energy storage within the optimization algorithm is possible using RHC integration.</description><subject>Computational modeling</subject><subject>Conics</subject><subject>Control systems</subject><subject>Convex Optimization</subject><subject>Dynamical systems</subject><subject>Dynamics</subject><subject>Energy storage</subject><subject>Job shop scheduling</subject><subject>Mathematical models</subject><subject>Optimal scheduling</subject><subject>Optimization</subject><subject>Programming</subject><subject>Radial Distribution System</subject><subject>Real-time Optimization</subject><subject>Stochastic processes</subject><issn>0093-9994</issn><issn>1939-9367</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkUFr3DAQhUVooNuk90Iugl568WZk2bJ1XLbdZCGQ0CRnoZXHWy22tZXktOkfyN-OzC4l5DSP4XvDYx4hXxjMGQN5-bBezHNgYp6XnImcn5AZk1xmkovqA5kBSJ5JKYuP5FMIOwBWlKyYkZfbfbS97uhKh0iXbojedVQPDb03v7AZOztsqWvpnfuDnn63IXq7GaN1A71_DhF7-hgmZD1E3HodsaE_0WAz7a6dt_8S-PZq0k_4dxrW0DvvkqfvE3xOTlvdBfx8nGfkcfXjYXmd3dxerZeLm8xwCTFjbS5YVdeCY1KyMvWm5E0tUGhemjxvhNGlgQ2aQrZSoIGi0tIUDOqibaqcn5Fvh7t7736PGKLqbTDYdXpANwbFaiag4GUFCf36Dt250Q8pnWKVBJFCCJ4oOFDGuxA8tmrv00P9s2KgpmZUakZNzahjM8lycbBYRPyPVykkcMZfAeGViso</recordid><startdate>201605</startdate><enddate>201605</enddate><creator>Moghadasi, Seyedmahdi</creator><creator>Kamalasadan, Sukumar</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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The main advantages of the proposed method are 1) global optimal scheduling with faster computation time; 2) dynamic models with online control within optimization routine; and 3) integration of uncertain resources and measurements. The effectiveness of this method is evaluated using modified IEEE 32-bus and IEEE 119-bus distribution test systems considering network constraints such as energy market interactions, storage dynamics, and uncertain model of wind generation. The efficiency of the proposed method compared to RHC ac optimal power flow (RHC-ACOPF) is also evaluated using real-time simulator. The results show that the proposed method outperforms the RHC-ACOPF and guarantees global optimal solution. 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| subjects | Computational modeling Conics Control systems Convex Optimization Dynamical systems Dynamics Energy storage Job shop scheduling Mathematical models Optimal scheduling Optimization Programming Radial Distribution System Real-time Optimization Stochastic processes |
| title | Optimal Fast Control and Scheduling of Power Distribution System Using Integrated Receding Horizon Control and Convex Conic Programming |
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