Short-term uncertainty should be properly modeled when the expansion planning problem in a power system is analyzed. Since the use of all available historical data may lead to intractability, clustering algorithms should be applied in order to reduce computer workload without renouncing accuracy representation of historical data. In this paper, we propose a modified version of the traditional K-means method that seeks to attain the representation of maximum and minimum values of input data, namely, the electric load and the renewable production in several locations of an electric energy system. The crucial role of depicting extreme values of these parameters lies in the fact that they can have a great impact on the expansion and operation decisions taken. The proposed method is based on the traditional K-means algorithm that represents the correlation between electric load and wind-power production. Chronology of historical data, which influences the performance of some technologies, is characterized though representative days, each one composed of 24 operating conditions. A realistic case study based on the generation and transmission expansion planning of the IEEE 24-bus Reliability Test System is analyzed applying representative days and comparing the results obtained using the traditional K-means technique and the proposed method.

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This paper addresses the transmission network expansion planning problem considering storage units under uncertain demand and generation capacity. A two-stage adaptive robust optimization framework is adopted whereby short-and long-term uncertainties are accounted for. This work differs from previously reported solutions in an important aspect, namely, we include binary recourse variables to avoid the simultaneous charging and discharging of storage units once uncertainty is revealed. Two-stage robust optimization with discrete recourse problems is a challenging task, so we propose using a nested column-and-constraint generation algorithm to solve the resulting problem. This algorithm guarantees convergence to the global optimum in a finite number of iterations. The performance of the proposed algorithm is illustrated using the Garver's test system.

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