Nowadays, power systems are transitioning to an increasing penetration of vast low-cost wind and solar generation in order to achieve the greenhouse-gas-emission reduction targets in the electricity sector, which will require system flexibility for balancing requirements to maintain system performance. The current technologies have limited technical capabilities to provide this flexibility, and new alternatives are required. In this context, energy storage is one of the most promising options that can deliver technical and economic benefits. However, modeling energy storage systems represents a challenge because they have a wide range of technologies from pumped hydro units to batteries, each one with different characteristics that make them more suitable either short- (e.g., hours) or long-term (e.g., months) applications. This thesis proposes optimization models that improve current operational and investment planning tools by a better consideration of short- and long-term operational decisions for different grid-level energy storage technologies that impact tactical and strategic planning in power systems. This thesis then tackles the energy storage operation and investment problem in the following aspects: ? Representation of Energy Storage Operation: we propose improvements in current decision support models to deal with short-term storage such as batteries and seasonal storage at the same time, including network-constrained analysis. In addition, it determines the main drawbacks of the traditional modeling approaches using an hourly unit commitment model as a benchmark for the comparison of the current and proposed models. ? Co-optimization of Energy Storage Technologies in hydrothermal dispatch models: we assess the impact of short-term energy storage decisions on the opportunity cost of long-term storage through the proposal of a new optimization model for hydrothermal coordination in which hourly opportunity costs or short-term signals are co-optimized with seasonal storage. ? Investment Decision Models for Energy Storage: we formulate and test the main modeling approaches to evaluate energy-storage-systems investment in power systems with high penetration of renewable energy sources. Moreover, we analyze the influence of transmission constraints, losses, and increased renewable energy penetration on planning energy-storage-systems allocation and investment. ? Investment Decision Models for Energy Storage using Power-based Unit Commitment: we improve current investment models by correctly modeling power system flexibility requirements that lever different energy storage investment. Moreover, we compare energy-based and power-based unit commitment models and analyze the main advantages and disadvantages for the energy storage investment decisions. The proposed models can support energy storage owners, investors, system operators, planning entities, and regulatory authorities in their decisions regarding energy storage in the future context of high share of variable renewable energy sources.
Descriptors: energy storage systems, generation expansion planning, hydrothermal dispatch, battery energy storage system, power-based unit commitment, optimization.
Universidad Pontificia Comillas. Madrid (España)
03 July 2019
D.A. Tejada (2019), Co-optimization of energy storage technologies in tactical and strategic planning models. Universidad Pontificia Comillas. Madrid (Spain).