Problems, models, and algorithms in data-driven energy demand management [electronic resource]
Keywords: Electrical Engineering.

A compelling vision for the electricity grid of the 21st century is that of a highly-instrumented system that integrates distributed generation from renewable and conventional sources where superior monitoring allows a targeted, localized, dynamic matching of demand and supply while maintaining a high degree of overall stability. To better monitor demand, utilities have recently deployed massive advanced sensing infrastructure (smart meters) to collect energy consumption data at fine (sub-hourly) time scales from large consumer populations; thus, there is urgent need formalize the new problems and develop the appropriate models, scalable algorithms, and methodologies that can leverage this new information to improve grid operations. The key tension in shaping demand is that while benefits from demand-side management programs are relevant in the aggregate (over many consumers), consumption change happens at the level of the indivdual consumer. As such, incentive schemes (e.g., dynamic pricing) that aim to change certain aspects of the average consumer's consumption may not be optimal for any particular} real consumer. Thus, the perspective this thesis takes is that of data-driven energy program targeting, i.e., using smart meter readings for identifying high-potential types of consumers for certain demand-response and energy-efficiency programs, and designing tailored controls and incentives to improve their usage behavior. This is as much a computational and engineering problem as a management and marketing one. The central contribution of this thesis is on methodology for quantifying uncertainty in individual energy consumption, and relating it to the potential for flexibility for the design and operation of certain demand-side programs. In particular, three algorithmic and modeling contributions are presented that are motivated by the question of comparing and benchmarking the impact and potential of individual consumers to providing flexibility for demand-side management. First, it is noted that individual consumption is empirically observed to be highly volatile; as such no matter how good a predictive model, part of consumption will remain uncertain. Here, this variability is shown to be related to the stress each consumer places on the grid (through their respective cost-of-service); moreover a scalable clustering algorithm is proposed to uncover patterns in variability as encoded in typical distribution functions of consumption. Second, a model of individual consumption is proposed that interprets smart meter readings as the observed outcome of latent, temperature-driven decisions to use either heating, air conditioning, or no HVAC at all; algorithms for learning such response models are introduced that are based on the maximum likelihood estimation framework. The dynamic consumption model is validated experimentally by emphasizing the intended end-use of statistical modeling when comparing with ground-truth data. A third methodological contribution leverages the statistical description of individual consumer response to weather to derive normative, tailored control schedules for thermally-sensitive appliances. These actions are optimal in the sense that they both satisfy individual effort constraints, and contribute to reducing uncertainty in the aggregate over a large population. In addition to the algorithmic and modeling contributions, this thesis presents at great length the application of the methods developed here to realistic situations of segmentation and targeting large populations of consumers for demand-side programs. We illustrate our models and algorithms on a variety of data sets consisting of heterogeneous sources - electricity usage, weather information, consumer attributes - and of various sizes, from a few hundred households in Austin, TX to 120,000 households in Northern California. We validate our dynamic consumption model experimentally, emphasizing the end purpose of decisions made using the outcome of the statistical representation of consumption. Finally, we discuss the two sides of the data coin - increased effectiveness in program management vs potential loss of consumer privacy - in an experimental study in which we argue that certain patterns in consumption as extracted from smart meter data may in some cases aid in predicting relevant consumer attributes (such as the presence of large appliances and lifestyles such as employment or children), but not many others. This, in turn, can enable the the program administrator or marketer to target those consumers whose actual data indicates that they might respond to the program, and may contribute to the debate on what consumers unwillingly reveal about themselves when using energy.