Fuel treatments, the reduction of forest biomass through mechanical removal or burning, are a flexible forest management tool used to address a variety of human and environmental concerns. Treatments can be used to reduce high severity fire, improve forest productivity and drought resilience, and increase streamflow. However, the effects of fuel treatments can be inconsistent and uncertain. Fuel treatment effects are sensitive to the treatment, the biophysical environment in which the treatment is done, and can be further altered by climate change. Climate change is already increasing wildfire size and frequency, drought, and straining water supplies in much of the Western US. Given that fuel treatments are likely to play a key role in current and future forest management, it is critical that we understand the full range of fuel treatment interactions with climate and effects on forests, water, and fire. Existing ecohydrologic models are limited in their ability to model fuel treatment effects because they do not account for both the within forest stand ecohydrologic effects of changes in forest structure and the hillslope-scale variation in radiation, water availability, and other meteorologic drivers. The ability to simulate heterogenous vegetation cover at stand scales is key to implementing fuel treatments like forest thinning. To address this gap, I adapted the Regional Hydro-Ecological Simulation System (RHESSYs) to include a new multiscale routing (MSR) approach, RHESSys-MSR. In addition to modeling within forest stand heterogeneity, MSR enables an additional layer of hydrologic routing that is included within existing topographic hillslope routing. Chapter 1 of this thesis describes the implementation of RHESSys-MSR, and Chapters 2 & 3 apply this model to investigate fuel treatment effects in a changing climate. In the first application of these methods (Chapter 2), I simulate a large set (13,500) of model scenarios varying treatment type, biophysical and climatic conditions for a Central California Sierra forest stand. Results show that plant accessible water storage capacity and vegetation type are dominant environmental controls that alter the effects of fuel treatments. More broadly I find that estimating the effect of fuel treatments based on only a single biophysical variable fails to capture the extent of possible treatment effects. In the second application (Chapter 3), I investigate the interactions between projected climate change and fuel treatment area on the effects of treatments on forest health, fire risk, and streamflow at the watershed scale. Results show that while fuel treatment effects can be noteworthy in the short term (< 5 years), projected climate change is likely to mask long term (20-year) fuel treatment effects. Fuel treatments and their effects are complex, spanning the environmental domains of forests, water, fire, and climate. Treatments are further complicated by the wide variety in the treatment itself, varying in how forest structure is affected, where it’s implemented, and how often. Model applications like RHESSys-MSR are critical to reducing this uncertainty and developing place-based estimates of fuel treatment effects that can support forest managers. The persistent challenges in understanding fuel treatments and their effects make any progress all the more essential, and as this research and more contributes to this understanding, we can make more informed forest management decisions for the future.

The goal of residential load management is to manipulate the electricity demand of a group of consumers to match some desired shape. Effective load management needs two types of controls: systemic and local. Systemic control manipulates the aggregate power by signaling the consumers. Local control determines how the power consumer reacts to the signals. I present a price responsive intelligent thermostat for use in local load management. Further, I explore two types of systemic control using electricity price as the control variable. The first approach is to identify the system using a low order model and apply traditional control techniques. The second approach makes use of game theory to auction the electricity to the consumers. Both methods enable reliable and robust high resolution load management.

People living in houses consume a substantial portion of total electricity consumption— 37% of U.S. electricity end use (Energy Information Administration (EIA) 2008)—which produces greenhouse gases. U.S. households exhibit extreme variability in energy consumption from one house to another. The variation in energy consumption from differences in climate and building characteristics is well-studied; however, the effect of various appliance end use and especially the variation in the behaviors of the people that use them is less understood. Yet, this variability is critical to the effective design of technology, efficiency, and/or demand response programs in order to reduce this consumption, especially during periods of peak electricity consumption. While many techniques have been used to simulate actual residential energy consumption using models, most fail to take into account the behavioral component that contributes to the wide spectrum of residential energy consumption.

Towards this end, we have developed the Residential Power Network Simulation (ResPoNSe) to capture the spectrum—not average—of the electrical consumption of California households over the course of a hot summer day. ResPoNSe models the electricity consumption of a thousand households in order to test different demand response scenarios. Distributions of household characteristics, numbers and types of appliances per house, power consumption of the appliances, and the duration these appliances are used provide a more realistic variation of energy consumption. In turn, this simulation tool can provide a model of the spectrum of consumer response to different efficiency, marketing, or demand response programs.

A research team at the University of California, Berkeley was tasked with addressing technical questions arising from the implementation of residential programmable communicating thermostats (PCTs) in California to meet updates to Title 24 building code related to PCTs. The purpose of the project was to perform research, development, and demonstration tasks to: validate the California Energy Commission’s PCT vision, answer technical issues related to system integration and implementation, recommend a suitable technology for a statewide one-way communication system for PCTs, and develop a methodology to study the impact of PCTs on electrical demand. The team performed several tasks to achieve these goals.

The team validated the Energy Commission’s vision by concluding that it was possible for manufacturers to develop PCTs that: met the proposed Title 24 system interface requirements, could be sold at a retail price less than $100, and could be manufactured within the Energy Commission’s planned Title 24 timeline. This conclusion was the result of a concept for a minimum functionality PCT that was published as a bill of materials and validated with a working proof-of-concept demonstration.

The team researched PCT system interfaces and identified technical issues related to system integration and implementation that should be considered as part of the policy, technology, and system design related to PCT implementation.

The team reviewed technologies that could be used for statewide PCT communication and recommended the Radio Data System (RDS) technology as a suitable solution. The team demonstrated the feasibility of this technology and developed a site survey tool and methodology to study its real-world performance.

Finally, the team developed a load group simulation to characterize the result of PCT pricing and control signals and used the simulation to study certain demand response scenarios. The team found that rebound peak from demand response events can be significant and that strategies to manage this peak should be investigated further.

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