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| A Distributionally Robust Optimization Scheduling Model Considering Higher-Order Uncertainty of Wind Power |
| Xia Peng1, Liu Wenying1, Zhang Yaoxiang1, Wang Weizhou2, Zhang Bolin2 |
1. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing 102206 China;
2. State Grid Gansu Electric Power Company Lanzhou 730050 China |
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Abstract Higher-order uncertainty of wind power probability distribution is ignored in traditional optimal dispatching methods considering wind power uncertainty, and there is further optimization space for operation cost and wind power consumption capacity. Therefore, a distributionally robust optimization scheduling model considering higher-order uncertainty of wind power was proposed in this paper. Firstly, a wind power higher-order uncertainty model was established by introducing cloud model theory, which could simultaneously describe the uncertainty of wind power and its probability distribution. On this basis, combined with distributionally robust optimization theory, taking the lowest comprehensive operation cost as optimal target, a distributionally robust optimization scheduling model was established. And then, multi-dimensional sequential operation theory was introduced to discretize the higher-order uncertainty cloud model of wind power, so as to transform distributionally robust optimization model into a two-stage nonlinear optimization model and simplified its solution. Finally the effectiveness of proposed model in improving optimal dispatching effect of wind power system are verified.
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Received: 05 July 2019
Published: 17 January 2020
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2020, Vol. 35 
