Search

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]

The objective of this paper is to propose a novel methodology to compute the benefit obtained by the individual transmission network users from each of the transmission expansion projects within an expansion plan. The benefits computed should be coherent with the technical and economic principles that underlie the development of the expansion plan. Thus, this methodology is based on the idea that the benefits produced by each project of a plan should be determined considering all projects jointly, instead of individually. Some benefits obtained by users from projects evolve continuously with the deployment of the expansion plan, while others are discrete, since they occur at certain points of the deployment of this plan. A separate Aumann-Shapley game is solved to allocate continuous benefits, and each discrete one. In the second case, the standard Aumann-Shapley algorithm for the allocation of benefits is modified to cope with the fact that the function of each user’s benefits is not continuous with the size of projects deployed. Two case studies are used to compare the methodology proposed with existing ones and demonstrate its applicability to real-life decision making processes. The results show that the methodology proposed is able to overcome problems detected in other methodologies, providing more accurate and sound results. The good properties of the methodology proposed make it applicable to problems related to network expansion regulation, such as the cost allocation of new investments. Although the methodology proposed is particularized to electric power systems, its concept and fundamentals can also be applied in other energy sectors, such as gas. [ABSTRACT FROM AUTHOR]

Electricity transmission contracts allocate scarce resources, allow hedging against locational price differences, and provide information to guide investment. However, ownership of transmission contracts might enhance market power of strategic generators. If the allocation mechanism for transmission contracts does not prevent generators from obtaining market power-enhancing contracts, explicit restrictions on owning transmission contracts might be required. To ensure that these restrictions do not interfere with the need to hedge for transmission price risk, one proposal is to define all transmission contracts to a balancing point at which energy is traded. Generators are only allowed to own transmission contracts toward this balancing point. Traders or load hedge the price risk from this balancing point to the location of load with transmission contracts. We propose an algorithm and apply it to the European electricity network to identify a balancing point that reduces market power of generation companies and is well connected. Market-level data or detailed information about demand are not required. The only critical input is assumptions on future transmission constraint patterns. [ABSTRACT FROM AUTHOR]