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The global drive for integration of RESs (renewable energy sources) means that TEP (transmission expansion planning) has to be carried out over geographically wide and large-scale networks under high levels of uncertainty. This leads to complex combinatorial TEP optimization problems, requiring a huge amount of OPF (optimal power flow) computations. The algorithm employed to calculate the OPF must be reasonably accurate but computationally very efficient, because it has to be run for a lot of operational conditions. Overly simplified OPF formulations are not adequate, since they neglect aspects which play a relevant role in TEP. Specifically, network losses significantly influence TEP solutions, but they are often disregarded due to their computational burden. Given their potential impact on the optimal TEP solution, the main focus of this paper is to find an appropriate losses model in the context of medium to long-term TEP for large-scale power systems. Keeping the balance between accuracy and computation time is essential in such problems. The paper presents two alternative linear losses models, as well as two variants of existing ones. These models are compared and tested using case studies, including small, medium and large-scale networks. Practical conclusions and recommendations are drawn from numerical results. [ABSTRACT FROM AUTHOR]

The global drive for integration of RESs (renewable energy sources) means they will have an increasing role in power systems. It is inevitable that such resources introduce more operational variability and uncertainty to system functioning because of their intermittent nature. As a result, uncertainty management becomes a critical issue in long-term TEP (Transmission Expansion Planning) in power systems which feature a significant share of renewable power generation, especially in terms of computational requirements. A significant part of this uncertainty is often handled by a set of operational states, here referred to as “snapshots”. Snapshots are generation—demand patterns that lead to OPF (optimal power flow) patterns in the network. A set of snapshots, each one with an estimated probability, is then used in network expansion optimization. In long-term TEP of large networks, the amount of operational states must be reduced to make the problem computationally tractable. This paper shows how representative snapshots can be selected by means of clustering, without relevant loss of accuracy in a TEP context, when appropriate classification variables are used for the clustering process. The approach relies on two ideas. First, snapshots are characterized by their OPF patterns instead of generation—demand patterns. This is simply because network expansion is the target problem, and losses and congestions are the drivers of network investments. Second, OPF patterns are classified using a “moments” technique, a well-known approach to address Optical Pattern Recognition problems. Numerical examples are presented to illustrate the benefits of the proposed clustering methodology. The method seems to be very promising in terms of clustering efficiency and accuracy of the TEP solutions. [ABSTRACT FROM AUTHOR]