Abstract:
This thesis examines industrial demand-side management over a co-optimized energy and reserve market from the perspective of a strategic consumer with flexibility in demand and ability to offer interruptible load. Industrial demand response is the participation of large consumers in electricity markets, through active management of their consumption in response to prices. Demand response not only enables large consumers of electricity to reduce their energy costs, but it also improves the reliability of the system, facilitates increased capacity of distributed energy resources within the market, and may enable deferrals in generation and transmission investments. Another important aspect of industrial demand response is its contribution to maintaining grid stability through offering ancillary services. For instance, major consumers with demand flexibility may offer interruptible load reserve, which enables them to earn revenue through their ability to shed load thus protecting the system against potential outages.What differentiates industrial demand-side participation from other forms of demand response is that a large manufacturer's decision on its consumption level can affect the energy and reserve prices. Therefore, we model price-making major consumers who anticipate the impacts of their actions on the market. Through a bi-level optimization model, we study the strategic behavior of large consumers in both energy and reserve markets under uncertainty. This optimization problem maximizes the expected utility of the major consumer, yielding admissible energy bid stacks and reserve offer stacks for each trading period. In order to apply this to real-world problems, our model is reformulated to a mixed-integer program, for which we offer tailor-made solution methods. In addition to presenting utility-driven co-optimization of energy and reserve, we propose stochastic cost-minimizing demand scheduling models, where finding the optimal load level for each trading period depends on the consumption in other time periods. We present a unique and novel stochastic multistage optimization method to compute optimal strategies using Lagrangian decomposition methods. This model is developed for major manufacturers who are required to achieve a total production level over a short- or long-term time horizon and can shift consumption of electricity by utilizing flexibility in their production schedules. Finally, we demonstrate implementation of our single- and multi-stage models for a large industrial consumer of energy in New Zealand. Through utilizing our proposed solution methodologies, we simulate our policies with historical data and compare the results with the cost of policies that are used in practice.
