Date of Award
Doctor of Philosophy (PhD)
Engineering and Public Policy
The future electricity grid is likely to be increasingly complex and uncertain due to the introduction of new technologies in the grid, the increased use of control and communication infrastructure, and the uncertain political climate. In recent years, the transactive energy market framework has emerged as the key framework for future electricity market design in the electricity grid. However, most of the work done in this area has focused on developing retail level transactive energy markets. There seems to be an underlying assumption that wholesale electricity markets are ready to support any retail market design. In this dissertation, we focus on designing wholesale electricity markets that can better support transactive retail market. On the highest level, this dissertation contributes towards developing tools and models for future electricity market designs. A particular focus is placed on the relationship between wholesale markets and investment planning. Part I of this dissertation uses relatively simple models and case studies to evaluate key impediments to flexible transmission operation. In doing so, we identify several potential areas of concern in wholesale market designs: 1. There is a lack of consideration of demand flexibility both in the long-run and in the short-run 2. There is a disconnect between operational practices and investment planning 3. There is a need to rethink forward markets to better manage resource adequacy under long-term uncertainties 4. There is a need for more robust modeling tools for wholesale market design In Part II and Part III of this dissertation, we make use of mathematical decomposition and agent-based simulations to tackle these concerns. Part II of this dissertation uses Benders Decomposition and Lagrangian Decomposition to spatially and temporally decompose a power system and operation problem with active participation of flexible loads. In doing so, we are able to not only improve the computational efficiency of the problem, but also gain various insights on market structure and pricing. In particular, the decomposition suggests the need for a coordinated investment market and forward energy market to bridge the disconnect between operational practices and investment planning. Part III of this dissertation combines agent-based modeling with state-machine based modeling to test various spot, forward, and investment market designs, including the coordinated investment market and forward energy market proposed in Part II of this dissertation. In addition, we test a forward energy market design where 75% of load is required to be purchased in a 2-year-ahead forward market and various transmission cost recovery strategies. We demonstrate how the different market designs result in different investment decisions, winners, and losers. The market insights lead to further policy recommendations and open questions. Overall, this dissertation takes initial steps towards demonstrating how mathematical decomposition and agent-based simulations can be used as part of a larger market design toolbox to gain insights into different market designs and rules for the future electricity grid. In addition, this dissertation identifies market design ideas for further studies, particularly in the design of forward markets and investment cost recovery mechanisms.
Tee, Chin Yen, "Market Design for the Future Electricity Grid: Modeling Tools and Investment Case Studies" (2017). Dissertations. 856.
Available for download on Friday, April 26, 2019