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A Distributed Control Strategy for Reactive Power Compensation in Smart Microgrids
Open accessAbstract
We consider the problem of optimal reactive power compensation for the minimization of power distribution losses in a smart microgrid. We first propose an approximate model for the power distribution network, which allows us to cast the problem into the class of convex quadratic, linearly constrained, optimization problems. We then consider the specific problem of commanding the microgenerators connected to a microgrid, in order to achieve the optimal injection of reactive power. For this task, we design a randomized, leader-less, gossip-like optimization algorithm. We show how a distributed approach is possible, where microgenerators need to have only a partial knowledge of the problem parameters and of the state, and can perform only local measurements. For the proposed algorithm, we provide conditions for convergence together with an analytic characterization of the convergence speed. The analysis shows that, in radial networks, the best performance is achieved when we command cooperation among microgenerators that are neighbors in the electric topology. Numerical simulations are included to validate both the proposed model and the analytic results about the performance of the proposed algorithm.
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Cites
Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000016 · Cited by 17,075
Distributed Subgradient Methods for Multi-Agent Optimization
Nedic, A., & Ozdaglar, A. (2009) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2008.2009515 · Cited by 3,719
Cited by
Tan, K. M., Ramachandaramurthy, V. K., & Yong, J. Y. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.09.012 · Cited by 872
Antoniadou-Plytaria, K. E., Kouveliotis-Lysikatos, I. N., Georgilakis, P. S., & Hatziargyriou, N. D. (2017) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2017.2679238 · Cited by 545
Microgrid control: A comprehensive survey
Sen, S., & Kumar, V. (2018) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2018.04.012 · Cited by 228
Karagiannopoulos, S., Aristidou, P., & Hug, G. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2019.2905348 · Cited by 162
Toward Distributed Energy Services: Decentralizing Optimal Power Flow With Machine Learning
Dobbe, R., Sondermeijer, O., Fridovich-Keil, D., Arnold, D., Callaway, D., & Tomlin, C. (2020) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2019.2935711 · Cited by 120
Metadata
- Authors
- Bolognani, S., & Zampieri, S.
- Venue
- IEEE Transactions on Automatic Control
- Published
- 2013-11
- Volume
- 58
- Issue
- 11
- Pages
- 2818-2833
- ISSN
- 0018-9286 (ISSN-L)
- 1558-2523
- OpenAlex
- W1974059941
- Cited by
- 228
- References
- 2
- FWCI
- 31.57
- Type
- journal article
- Language
- en
- Open access
- green
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