UC Berkeley

Motivated by environmental and energy security concerns, many states and countries have enacted legislation calling for increased renewable adoption in the electricity sector. Unlike conventional fossil fuel-based electricity sources, renewable resources such as wind and solar power are characterized by intermittent, uncertain, and non-dispatchable output. Successfully addressing this variability in renewable generation is a prerequisite for deep renewable penetration. The prevailing paradigm in most power systems, exemplified by programs such as the Participant Intermittent Renewable Program (PIRP) in California, is one where the system operator accepts all renewable generation and absorbs the attendant variability through operating reserves. This approach works today at modest renewable generation levels but will result in untenable increases in reserve requirements tomorrow when renewables serve a sizeable fraction of total electric load. Hence, meeting ambitious renewable penetration targets will require the implementation of a number of variability mitigation strategies across the entire spectrum of power system operations. In this dissertation, we identify and explore areas in which control and optimization techniques can help provide some of these solutions.

One such mitigation strategy is the coordinated aggregation of deferrable loads and storage, in which load is tailored to match variable supply. We investigate methods of exploiting these demand-side capabilities by developing and evaluating algorithms for the real-time scheduling of these flexible resources. We note that the benefits of coordinated aggregation can be achieved at modest levels of both deferrable load participation and flexibility. We also provide an analytical framework for understanding how these resources influence the costs of meeting load requirements incurred in wholesale electricity markets. Specifically, we explore the interplay between deferrable load scheduling and cost-minimizing procurements of bulk power and reserve capacity made in the day-ahead forward market.

Another solution we consider, specific to wind power, involves curtailing generator output in certain situations. We explore how a wind power producer - subject to financial penalties for imbalances from contracted amounts - might leverage power curtailment capability to mitigate financial risk arising from price and production uncertainty. In particular, we analytically quantify the economic benefit derived from curtailment as an explicit function of expected prices, and compute empirical estimates of this curtailment benefit using price data from the various power system operators.