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Active distribution networks planning with high penetration of wind power
Mokryani, G Hu, YF Pillai, P Rajamani, HS
Mokryani, G
Hu, YF
Pillai, P
Rajamani, HS
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2016-12-05
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Abstract
© 2016 Elsevier Ltd In this paper, a stochastic method for active distribution networks planning within a distribution market environment considering multi-configuration of wind turbines is proposed. Multi-configuration multi-scenario market-based optimal power flow is used to maximize the social welfare considering uncertainties related to wind speed and load demand and different operational status of wind turbines (multiple-wind turbine configurations). Scenario-based approach is used to model the abovementioned uncertainties. The method evaluates the impact of multiple-wind turbine configurations and active network management schemes on the amount of wind power that can be injected into the grid, the distribution locational marginal prices throughout the network and on the social welfare. The effectiveness of the proposed method is demonstrated with 16-bus UK generic distribution system. It was shown that multi-wind turbine configurations under active network management schemes, including coordinated voltage control and adaptive power factor control, can increase the amount of wind power that can be injected into the grid; therefore, the distribution locational marginal prices reduce throughout the network significantly.
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Mokryani, G., Hu, Y. F., Pillai, P. and Rajamani, H-S. (2016) Active distribution networks planning with high penetration of wind power, Renewable Energy, 104, pp. 40-49.
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Journal article
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en
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This is an accepted manuscript of an article published by Elsevier in Renewable Energy on 05/12/2016, available online: https://doi.org/10.1016/j.renene.2016.12.007
The accepted version of the publication may differ from the final published version.
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0960-1481
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This work was supported in part by the SITARA project funded by British Council and the Department for Business, Innovation and Skills, UK and in part by the University of Bradford, UK under the CCIP grant 66052/000000.