Abstract
This paper studies economic power dispatch strategy in a smart grid by using the distributed consensus protocol in multi-agent systems, where there are many generation units working cooperatively under a local neighboring area to achieve a minimum solution for the optimization problem of the total cost function. At first, the analysis indicates that the total cost for generators in a smart grid can reach its minimal value if the incremental cost for all generation units can achieve consensus and the balance between supply and demand of powers is satisfied. Then, by designing a distributed consensus protocol in multi-agent systems for the incremental cost with appropriate initial conditions, incremental cost consensus can be reached for all the generation units and the balance for the powers can also be kept. Thus, the optimization problem for the cost function of all generation units can be solved. Finally, some simulation examples are performed to verify the analysis in this paper. © 2015 IEEE.
| Original language | English |
|---|---|
| Title of host publication | 2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015 |
| Publisher | IEEE |
| ISBN (Print) | 9781479978625 |
| DOIs | |
| Publication status | Published - 8 Sept 2015 |
| Externally published | Yes |
| Event | 10th Asian Control Conference, ASCC 2015 - Kota Kinabalu, Malaysia Duration: 31 May 2015 → 3 Jun 2015 |
Publication series
| Name | 2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015 |
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Conference
| Conference | 10th Asian Control Conference, ASCC 2015 |
|---|---|
| Place | Malaysia |
| City | Kota Kinabalu |
| Period | 31/05/15 → 3/06/15 |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Funding
This work was supported by the National Natural Science Foundation of China under Grant Nos. 61322302, 61025017, and 61304168, the Six Talent Peaks of Jiangsu Province of China under Grant No. 2014-DZXX004, and the Australian Research Council (ARC) Discovery Scheme under Grant DP140100544.
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