How can cities efficiently and affordably undergo effective and dramatic decarbonization of buildings and vehicles? Can these strategies promote equity and scale across the urban environment worldwide? The EcoBlock in Oakland, California seeks to answer this urgent environmental, social, and technical question by designing, testing, and deploying community-scale energy and water systems. These innovative systems combine energy and water efficiency and electrification at the building scale with an electrical system that integrates rooftop solar, block-scale storage, electric vehicle (EV) charging, and a smart microgrid that optimizes supply and demand at the block-scale.

This paper presents the approach and strategies when moving from design to implementation deploying this novel prototype toward industrializing city-wide, residential microgrids that generate their own clean, renewable power for homes and EVs. Scaling retrofits requires coordination from evaluation, product selection, permitting and installation involving contractors, utilities, consultants and various Authorit(ies) Having Jurisdiction (AHJ). Improvements at homes interconnect with block scale microgrid and EV charging in an existing block and social dynamic. This paper will discuss opportunities and pitfalls to implementing block scale electrification and microgrid to achieve environmental benefits of emission savings and resiliency, utility cost benefits, social benefits of neighborhood network, and comfort and health benefits in each home.

This study developed several technologies to enable demand response for residential electricity customers in order to reduce peak consumption. First, along with a team of students and professors, I designed and tested the Demand Response Electrical Appliance Manager (DREAM). This wireless network of sensors, actuators, and controller with a user interface intelligently controls a residential heating and cooling system and informs people of their energy usage. Secondly, I evaluated machine-learning to predict a person’s seasonal temperature preferences by analyzing existing data from office workers. The third part of the research developed an algorithm that generated temperature setpoints based on outdoor temperature and compared simulated energy use with these versus the default setpoints of a programmable thermostat. Finally, I developed and tested a user interface for a thermostat and in-home energy display. This part of the study tested the effects of both energy versus price information and the context of sponsorship on the behavior of subjects, and also surveyed subjects on the usefulness of various displays.

The wireless network succeeded in providing detailed data to enable an intelligent controller and provide feedback to the users. The learning algorithm showed mixed results. The adaptive temperature setpoints saved energy in both annual and summertime simulations. The context in which I introduced the DREAM interface affected behavior, but the type of information displayed did not. The subjects responded that appliance-level feedback and tools that provided choices would be useful in a dynamic tariff environment.

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.