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This paper presents a new method for allocating transmission system costs in hydro prevailing energy markets. The proposed approach is based on nodal pricing, and it considers multiple hydrological scenarios obtained from optimized energy models. These models are much closer to the operational reality of hydro dominant systems and more adequate to the transmission operation planning. The nodal pricing strategy comprises two steps to recover the total costs of the transmission system. The first one calculates the charges corresponding to the utilized network capacity, estimated at a specified operating condition, and it brings the locational signal to the tariff. In the second step, the charges related to the transmission capacity available but not used in the system are assessed by a postage stamp. This latter step accomplishes the task of recovering the total transmission charges and defining the final tariff for specific operating dispatching. On the other hand, the optimized energy models, based on stochastic dual dynamic programming, are used to minimize the operating costs while penalizing possible energy spillages. These models create monthly sequences of hydrothermal coordinated generating scenarios and dispatching conditions, which are used by the nodal pricing strategy to produce the generation/load charges and tariffs and the corresponding statistics. The proposed method, combining the nodal pricing strategy and the optimized energy model, is applied to a Brazilian network configuration and the results are fully discussed. Moreover, probabilistic analyses are also performed to describe the annual costs of transmission of thermoelectric and hydroelectric plants and industrial electrointensive consumers installed in different areas of the network. [ABSTRACT FROM AUTHOR]

Summary: In this paper, the static transmission expansion planning (TEP) problem is solved for real large electrical networks. Different generation and load scenarios are considered and the "N‐1" security criterion is ensured. The solution of the problem is achieved based on a constructive metaheuristic algorithm (CMA‐TEP) specially developed for large systems. The proposed method performs a gradual and parallel process of constructing solutions (expansion plans), which incorporates heuristics and procedures generally used by planning engineers. Operational information for the intact network condition and under contingencies is used during the constructive process, to identify reinforcements to be added (most attractive) to or removed (less effective) from the network being set up. Moreover, mechanisms of recombination, perturbation, selection, refinement, and depuration of solutions are also employed during the constructive process. The linear DC network model, including ohmic transmission losses, is used throughout the entire constructive process. The robustness and efficiency of the proposed CMA‐TEP method is tested in a configuration of the Brazilian network, considering two hydrological scenarios: dry and humid. Comparisons based on statistical performance indices are also carried out with specialized genetic algorithms. [ABSTRACT FROM AUTHOR]

Summary: Composite generation and transmission system reliability evaluation allows the assessment of the risks of system operation failure, taking into account the uncertainties associated with the availability of equipment. One of the great challenges faced in the use of techniques based on probabilistic assessment during the planning stages is related to the required computational costs. Depending on the reliability levels of the system under study and on the grid size, a large number of operation performance analyzes are necessary. In this sense, this article proposes a new and simple method to efficiently evaluate the composite reliability of electrical power networks. The nonsequential Monte Carlo simulation (MCS) method is combined with unsupervised machine learning (UML) techniques to reduce the computational effort involved in the process of estimating composite reliability indices. The proposed approach allows different unsupervised techniques to be employed, in order to obtain significant reductions in CPU times, without losing the accuracy of the estimated indices. The IEEE‐RTS system, considering the original load and generation and its modified version with the transmission network stressed, in addition to a real large system, is used for evaluating the performance of the proposed method. The results obtained with the use of three different classification techniques (Kohonen self‐organizing map, K‐means, and K‐medoids) are presented and analyzed. [ABSTRACT FROM AUTHOR]