A statistical approach to transmission network expansion planning

The complexity of the modern power system and the deregulation of the electric power industry have introduced more challenges to transmission network planners. In a large-scale power system, it is almost impossible to manually identify candidate expansion routes from thousands of possible transmission routes. On the other hand, searching the whole route space can be time-consuming and sometimes infeasible. Another significant challenge encountered by transmission planners is the increasing uncertainty involved in the planning process, which is the consequence of market deregulation. To meet the above challenges, a novel approach to transmission expansion planning (TEP) is proposed in this paper. A novel algorithm is developed to identify the transmission routes that are highly relevant to the system stability. These transmission routes will be selected as the candidate routes in the planning process, so as to significantly reduce the search space. A time series model based on advanced regression techniques is proposed to estimate the random uncertainties in the planning process. The non-random uncertainties, on the other hand, are modelled by constructing a series of future scenarios. The proposed method selects the most flexible transmission plan, which has least adaptation cost. Comprehensive case studies are conducted to demonstrate that the proposed method is effective. (8 pages)