This paper targets the unexplored problem of demand response within the context of power-grids that are allowed to regularly enforce blackouts as a mean to balance supply with demand:highly-stressed grids. Currently these utilities use as a cyclic and binary (power no-power) schedule over consumer groups leading to significant wastage of capacity and long hours of no-power. We present here a novel building DLC system, Aashiyana, that can enforce several user-defined low-power states. We evaluate distributed and centralized load-shedding schemes using Aashiyana that can, compared to current load-shedding strategy, reduce the number of homes with no power by 80 for minor change in the fraction of homes with full-power.
The second area of research focuses on designing appliance or home level power reduction --- while considering the comfort and ease of home owners @cite_7 @cite_15 @cite_19 @cite_5 . Both Yupik @cite_7 and n-Plug @cite_15 propose adding smart plugs to a few (deferable) appliances, whose usages patterns are monitored to present appropriate slack when a grid-stress event (demand peak) is indicated, using prices in Yupik and frequency in n-Plug. Smartcap @cite_20 uses programmable switches or smart-appliances to control background appliances using a least-slack-first algorithm to flatten any peak. @cite_19 evaluate how peaks can be flattened if the elastic component is allowed to be programmatically controlled. All these work seek to flatten load such that user prefernces are minimally affected; they fail to leverage (as they do not consider the problem domain) the experience of consumers in highly stressed grid that consequently face full blackouts for @math 12hrs a day @cite_21 . Furthermore, these are all primarily peak-shifting algorithms; for a highly-stressed grid there is .
@cite_7 @cite_21 @cite_19 @cite_5 @cite_15 @cite_20
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Flattening household electricity demand reduces generation costs, since costs are disproportionately affected by peak demands. While the vast majority of household electrical loads are interactive and have little scheduling flexibility (TVs, microwaves, etc.), a substantial fraction of home energy use derives from background loads with some, albeit limited, flexibility. Examples of such devices include A Cs, refrigerators, and dehumidifiers. In this paper, we study the extent to which a home is able to transparently flatten its electricity demand by scheduling only background loads with such flexibility. We propose a Least Slack First (LSF) scheduling algorithm for household loads, inspired by the well-known Earliest Deadline First algorithm. We then integrate the algorithm into Smart-Cap, a system we have built for monitoring and controlling electric loads in homes. To evaluate LSF, we collected power data at outlets, panels, and switches from a real home for 82 days. We use this data to drive simulations, as well as experiment with a real testbed implementation that uses similar background loads as our home. Our results indicate that LSF is most useful during peak usage periods that exhibit “peaky” behavior, where power deviates frequently and significantly from the average. For example, LSF decreases the average deviation from the mean power by over 20 across all 4-hour periods where the deviation is at least 400 watts. Demand response (DR) programs encourage end-use customers to alter their power consumption in response to DR events such as change in real-time electricity prices. Facilitating household participation in DR programs is essential as the residential sector accounts for a sizable portion of the total energy consumed. However, manually tracking energy prices and deciding on how to schedule home appliances can be a challenge for residential consumers who are accustomed to fixed price electricity taris. In this work, we present Yupik, a system that helps users respond to real-time electricity prices while being sensitive to their context and lifestyle. Yupik combines sensing, analytics, and optimization to generate appliance usage schedules that may be used by households to minimize their energy bill as well as potential lifestyle disruptions. Yupik uses jPlugs, appliance level energy metering devices, to continuously monitor the power usage by various home appliances. The consumption patterns as well as data from external sources are analyzed using data mining algorithms to infer user's preferred usage profile. Using the preferred profile as a reference, Yupik's optimization engine generates multiple usage plans that attempt to minimize energy and inconvenience costs. Some of Yupik's capabilities are demonstrated with the help of preliminary data collected from a home that was instrumented with jPlugs to monitor the power usage of a few devices. Unlike prior work on demand management, which typically requires industrial loads to be turned off during peak times, this paper studies the potential to carry out demand response by modifying the elastic load components of common household appliances. Such a component can decrease its instantaneous power draw at the expense of increasing its duration of operation with no impact on the appliance's lifetime. We identify the elastic components of ten common household appliances. Assuming separate control of an appliance's elastic component, we quantify the relationship between the potential reduction in aggregate peak and the duration required to complete the operation of appliances in four geographic regions: Ontario, Quebec, France and India. We find that even with a small extension to the operation duration of appliances, peak demand can be significantly reduced in all four regions both during winter and summer. For example, during winter in Quebec, a nearly 125 MW reduction in peak demand can be obtained with just a 10 increase in appliance operation duration. We conclude that exploiting appliance elasticity to reduce peak power demand should be an important consideration for appliance manufacturers. From a policy perspective, our study gives regulators the ability to quantitatively assess the impact of requiring manufacturers to conform to "smart appliance" standards. The Indian electricity sector, despite having the world's fifth largest installed capacity, suffers from a 12.9 peaking shortage. This shortage could be alleviated, if a large number of deferrable loads, particularly the high powered ones, could be moved from on-peak to off-peak times. However, conventional DSM strategies may not be suitable for India as the local conditions usually favor only inexpensive solutions with minimal dependence on the pre-existing infrastructure. In this work, we present nPlug, a smart plug that sits between the wall socket and deferrable loads such as water heaters, washing machines, and electric vehicles. nPlugs combine real-time sensing and analytics to infer peak periods as well as supply-demand imbalance and reschedule attached appliances in a decentralized manner to alleviate peaks whenever possible. They do not require any manual intervention by the end consumer nor any enhancements to the appliances or existing infrastructure. Some of nPlug's capabilities are demonstrated using experiments on a combination of synthetic and real data collected from plug-level energy monitors. Our results indicate that nPlug can be an effective and inexpensive technology to address the peaking shortage.