Low-power wireless promises increased flexibility and reduced costs in cyber-physical systems. To reap these benefits, protocols must deliver packets reliably within real-time deadlines across resource-constrained devices, while being adaptive to changes in network state and application requirements. Existing approaches do not solve these four challenges simultaneously, as they use a localized approach or critically depend on time-varying network state and its non-deterministic evolution. By contrast, we claim a global approach that is agnostic to network state overcomes these limitations. The Blink protocol proves this claim by providing hard guarantees on end-to-end deadlines of received packets in multi-hop low-power wireless networks, while seamlessly handling changes in network state and application requirements. We build Blink on an existing non real-time protocol, and design novel scheduling algorithms based on the earliest deadline first policy. Using a dedicated priority queue data structure, we demonstrate a viable implementation of our algorithms on resource-constrained devices. Experiments show that Blink: (i) meets 100% of the deadlines of received packets; (ii) delivers 99.97% of packets on a 94-node test-bed; (iii) minimizes communication energy consumption within the limits of the protocol we build upon; (iv) supports deadlines as small as 100 ms; and (v) executes up to 4.1 faster than conventional scheduler implementations on popular micro-controllers.
The popularity of rooftop solar for homes is rapidly growing. However, accurately forecasting solar generation is critical to fully exploiting the benefits of locally-generated solar energy. In this paper, we present two machine learning techniques to predict solar power from publicly-available weather forecasts. We use these techniques to develop SolarCast, a cloud-based web service, which automatically generates models that provide customized site-specific predictions of solar generation. SolarCast utilizes a ``black box'' approach that requires only i) a site's geographic location and ii) a minimal amount of historical generation data. Since we intend SolarCast for small rooftop deployments, it does not require detailed site- and panel-specific information, which owners may not know, but instead automatically learns these parameters for each site. We evaluate the accuracy of SolarCast's different algorithms on two publicly available datasets, each containing over one hundred rooftop deployments with a variety of attributes, e.g., climate, tilt, orientation, etc. We show that SolarCast learns a more accurate model using much less data (~1 month) than prior SVM-based approaches, which require ~3 months of data. SolarCast also provides a programmatic API, enabling developers to integrate its predictions into energy-efficiency applications. Finally, we present two case studies of using SolarCast to demonstrate how real-world applications can leverage its predictions. We first evaluate a ``sunny" load scheduler, which schedules a dryer's energy usage to maximally align with a home's solar generation. We then evaluate a smart solar-powered charging station, which can optimally charge the maximum number of electric vehicles (EVs) on a given day. Our results indicate that a representative home is capable of reducing its grid demand up to 40% by providing a modest amount of flexibility (of ~5 hours) in the dryer's start time with opportunistic load scheduling. Further, our charging station uses SolarCast to provide EV owners the amount of energy they can expect to receive from solar energy sources.
Reducing peak demands and achieving a high penetration of renewable energy sources are important goals in achieving a smarter grid. To reduce peak demand, utilities are introducing variable rate electricity prices to incentivize consumers to manually shift their demand to low-price periods. Consumers may also use energy storage to automatically shift their demand by storing energy during low-price periods for use during high-price periods. Unfortunately, variable rate pricing provides only a weak incentive for distributed energy storage and does not promote its adoption at large scales. In this paper, we present the storage adoption dilemma to capture the problems with incentivizing energy storage using variable rate prices. To address the problem, we propose a simple pricing scheme, called flat-power pricing, which incentivizes consumers to shift small amounts of load to flatten their demand, rather than shift as much of their power usage as possible to low-price, off-peak periods. We show that, compared to variable rate pricing, flat-power pricing i) reduces consumers' upfront capital costs, since it requires significantly less storage capacity per consumer, ii) increases energy storage's return-on-investment, since it mitigates free riding and maintains the incentive to use energy storage at scale, and iii) uses aggregate storage capacity within 31% of an optimal centralized approach. In addition, unlike variable rate pricing, we also show that, unlike variable rate pricing, flat-power pricing incentivizes intelligent scheduling of elastic background loads, such as air conditioners and heaters, to reduce peak demand. We evaluate our approach using real smart meter data from 14,000 homes in a small town.
In this paper, we perform a comprehensive survey to the technical aspects related to the implementation of demand response and smart buildings. Specifically, we discuss various smart loads such as heating, ventilating, and air-conditioning (HVAC) systems and plug-in electric vehicles (PEVs), the power architecture with multi-bus characteristics, different control algorithms such as the hybrid centralized and decentralized control and the distributed coordination among buildings, the communication technologies and network architectures, and the potential cyber-physical security issues and possible mechanisms for enhancing the system security at both cyber and physical layers. The current status of the demand response in United States, Europe, Japan, and China is reviewed, and the benefits, costs, and challenges of implementing and operating demand response and smart buildings are also discussed.