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This paper provides a new methodology to compute a reduced but efficient set of candidate lines in a transmission expansion planning (TEP) context. Considering this reduced set of candidate lines should lead to similar investment decisions as if all the possible ac and dc candidate lines that could be installed in the network were considered. A first “hybrid” relaxed TEP problem is solved. Based on this initial solution, a new relaxed TEP problem is iteratively solved in which the dc load flow (DCLF) model is enforced to a certain extent in the partially expanded ac corridors. Once a convergence threshold has been reached, an upper bound of the number of candidate ac and dc lines to install in each corridor can be defined. This process results in a compact search space. Our algorithm has been implemented in General Algebraic Modeling Software (GAMS) and has been tested on a case study based on the European power system. The method produces very promising results and in the considered case study, leads to a very efficient investment. [ABSTRACT FROM AUTHOR]

This paper provides a new methodology to compute a reduced but accurate network representation in a transmission expansion planning (TEP) context. Considering this reduced network should lead to the same investment decisions as if the whole original network were considered. A set of relevant lines to be preserved is defined based on a proxy of the TEP solution. An optimal network partition, resulting from solving the multicut problem, is computed in such a way that the two ends of each of these relevant lines are allocated to two different areas. An iterative Kron reduction is then applied to each area to eliminate most of the buses that are not connected to any interarea line. This two-step process results in a compact but representative reduced network. Our algorithm has been implemented in General Algebraic Modelling Software and Matrix Laboratory and has been tested on the standard IEEE 118 bus system and a case study based on the European power system. The method produces very promising results and, in the considered case studies, leads to the same, or equally efficient, investment decisions and essentially the same total costs as when considering the whole original network. [ABSTRACT FROM AUTHOR]

This paper provides a novel technique to represent in a reduced, or compact, way temporal variability in transmission expansion planning (TEP). This reduction is handled by means of “snapshot selection.” Instead of taking into account all the possible operational states and their associated optimal power flow, a reduced group of them is selected that is representative of all the states that should have an influence on investment decisions. Considering this reduced group of operational states should lead to the same investment decisions as if all the snapshots in the target year were considered. Original operational states are compared in the space of the benefits produced by potential reinforcements considered, which are relevant drivers for investment decisions. The benefits produced by these potential reinforcements are computed based on the incremental change in operation costs resulting from their installation. A clustering algorithm is used to group together those operational states where similar line benefits are realized. Our algorithm has been tested on a modified version of the standard IEEE 24 bus system. The method produces promising results and proves to be more efficient than other snapshot selection methods used until now in computing an accurate enough selection of snapshots representing system operation variability in TEP. [ABSTRACT FROM AUTHOR]

Transmission charges aim to recover the cost of transmission network investments and provide efficient locational signals to new generators. In this paper, we investigate the effect of these charges on the development of new generation capacities in the system. Generation expansion planning is decided by strategic generation planners (SGPs) trying to maximize their profits, while transmission line investments are planned by a central planner and regulatory body aimed at minimizing the overall operation and network investment costs of the system. Regulatory transmission charges (RTCs) are calculated according to the marginal responsibility of generation investment on transmission network investment costs. An iterative algorithm is proposed to model the interaction taking place between the central planner and SGPs. The developed methodology is applied to a 2-node illustrative example and the IEEE-RTS96, and effects of RTCs on investment decisions of SGPs are analyzed. [ABSTRACT FROM AUTHOR]