|dc.description.abstract||Transmission expansion planning is a challenging task that affects all aspects of the power generation system in different ways. Since the deregulation of the power industry, the competition between power generators has changed the way the national transmission grid is used. As a result, in addition to traditional power system planning that focuses on reliability issues, planning measures to alleviate congestion must now also be taken into account. This dissertation therefore considers transmission expansion planning designed to reduce the economic cost of congestion on the power system.
Here, economic transmission expansion planning, which minimizes the total investment cost as well as the congestion cost, is modeled using a multi-period decision framework and a multi-period decision framework that helps to calculate the equivalent cost of operation during the planning timeframe is applied. A Benders decomposition algorithm is then used to solve the resulting nonlinear mixed-integer problem. By applying the multi-period framework and solving the transmission expansion planning model, an investment plan that optimizes the entire power grid from a social welfare perspective can therefore be obtained.
The new model proposed here will be particularly useful for transmission planners who are responsible for making long-term decisions regarding power network operations. The dissertation goes on to define measures that can be used to compare investment plans under the proposed decision framework. Transmission expansion planning considering uncertainty is also investigated. First, a Monte Carlo sampling approach is used to generate possible scenarios for future market conditions after which a set of alternative investment plans are constructed by solving the multi-period transmission expansion model for each scenario. It is then possible to select the best alternative plan using a statistical comparison analysis. The results of these case studies show that the proposed multi-period model and the decision model considering uncertainty are flexible enough to handle large and realistic power networks.||en_US