Abstract
Ultra High Voltage (UHV) transmission technology is the main regional power interconnection method in China. In addition, renewable energy sources (RES) in China are characterized by centralized distribution. In view of the cross-regional flexible scheduling of large-scale grid-connected RES, we build a day-ahead optimization model which considers the generation economy and robustness of transmission for each regional grid. The model considers the spatial clustering effect of RES and loads, uses robust optimization theory to construct uncertainty sets, and introduces uncertain spatial constraint parameters to compensate for the insufficiency of traditional robust optimization. Furthermore, a co-evolutionary algorithm filter-based is proposed to solve above the mixed-integer non-convex nonlinear programming model. Taking the modified IEEE-39 nodes as an example, the results show that the system cost can be reduced by 21.9% by selecting the confidence probability of uncertain variables of both load and source under stable operation, while the prediction accuracy has a significant impact on the system cost when the confidence probability is smaller, and a better operation scheme can be obtained by increasing the space constraint parameters. Finally, the feasibility of the proposed model and algorithm are verified on the Hami power grid in Xinjiang, China.
Highlights
Ultra High Voltage (UHV) transmission technology has become the main physical method of regional power interconnection in China [1]
The cross-regional interconnected power grid (CRIG) [2], which has been a key driver of Chinese development, has evolved through a complex and lengthy process that has been influenced by numerous factors
Using UHV transmission, the CRIG system can effectively alleviate imbalances between the power supply and demand across regions that result from large-scale and longdistance transmission, promoting the rapid development of renewable energy sources (RES)
Summary
Ultra High Voltage (UHV) transmission technology has become the main physical method of regional power interconnection in China [1]. With the development of large-scale RES integration and multi-energy coupling systems, the algorithm may terminate prematurely due to the lack of global optimization ability in the face of different complex optimization problems of modern power dispatching. The a day-ahead optimization model considers the spatial clustering effect of RES and loads, uses robust optimization theory to construct uncertainty sets, and introduces uncertain spatial constraint parameters to overcome the shortcomings of traditional robust optimization while reasonably reducing the rotating reserve capacity and the blindness of dispatching cost. We selected two interconnected modified IEEE 39-bus systems as the cross-area dispatching model, analyzed the dynamic dependence of uncertain variables on system economy and robustness, and explore most economical and reliable scheduling scheme under different decisions’ demands by adjusting the parameters that restrict the aggregation of uncertainties, such as prediction accuracy and confidence probability etc.
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