Aditya Mishra

SmartCap: Flattening peak electricity demand in smart homes

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.

SmartCharge: cutting the electricity bill in smart homes with energy storage

Market-based electricity pricing provides consumers an opportunity to lower their electric bill by shifting consumption to low price periods. In this paper, we explore how to lower electric bills without requiring consumer involvement using an intelligent charging system, called SmartCharge, and an on-site battery array to store low-cost energy for use during high-cost periods. SmartCharges algorithm reduces electricity costs by determining when to switch the homes power supply between the grid and the battery array. The algorithm leverages a prediction model we develop, which forecasts future demand using statistical machine learning techniques. We evaluate SmartCharge in simulation using data from real homes to quantify its potential to lower bills in a range of scenarios. We show that typical savings today are 10-15%, but increase linearly with rising electricity prices. We also find that SmartCharge deployed at only 22% of 435 homes reduces the aggregate demand peak by 20%. Finally, we analyze SmartCharges installation and maintenance costs. Our analysis shows that battery advancements, combined with an expected rise in electricity prices, have the potential to make the return on investment positive for the average home within the next few years.

The case for efficient renewable energy management in smart homes

Distributed generation (DG) uses many small on-site energy sources deployed at individual buildings to generate electricity. DG has the potential to make generation more efficient by reducing transmission and distribution losses, carbon emissions, and demand peaks. However, since renewables are intermittent and uncontrollable, buildings must still rely, in part, on the electric grid for power. While DG deployments today use net metering to offset costs and balance local supply and demand, scaling net metering for intermittent renewables to many homes is difficult. In this paper, we explore a different approach that combines residential TOU pricing models with on-site renewables and modest energy storage to incentivize DG. We propose a system architecture and control algorithm to efficiently manage the renewable energy and storage to minimize grid power costs at individual buildings. We evaluate our control algorithm by simulation using a collection of real-world data sets. Initial results show that the algorithm decreases grid power costs by 2.7X while nearly eliminating grid demand peaks, demonstrating the promise of our approach.

Sharing renewable energy in smart microgrids

Renewable energy harvested from the environment is an attractive option for providing green energy to homes. Unfortunately, the intermittent nature of renewable energy results in a mismatch between when these sources generate energy and when homes demand it. This mismatch reduces the efficiency of using harvested energy by either i) requiring batteries to store surplus energy, which typically incurs ~20% energy conversion losses; or ii) using net metering to transmit surplus energy via the electric grids AC lines, which severely limits the maximum percentage of possible renewable penetration. In this paper, we propose an alternative structure wherein nearby homes explicitly share energy with each other to balance local energy harvesting and demand in microgrids. We develop a novel energy sharing approach to determine which homes should share energy, and when, to minimize system-wide efficiency losses. We evaluate our approach in simulation using real traces of solar energy harvesting and home consumption data from a deployment in Amherst, MA. We show that our system i) reduces the energy loss on the AC line by 60% without requiring large batteries, ii) scales up performance with larger battery capacities, and iii) is robust to changes in microgrid topology.

Scaling distributed energy storage for grid peak reduction

Reducing peak demand is an important part of ongoing smart grid research efforts. To reduce peak demand, utilities are introducing variable rate electricity prices. Recent efforts have shown how variable rate pricing can incentivize consumers to use energy storage to cut their electricity bill, by storing energy during inexpensive off-peak periods and using it during expensive peak periods. Unfortunately, variable rate pricing provides only a weak incentive for distributed energy storage and does not promote its adoption at scale. In this paper, we present the storage adoption cycle to describe the issues with incentivizing energy storage using variable rates. We then propose a simple way to address the issues: augment variable rate pricing with a surcharge based on a consumers peak demand. The surcharge encourages consumers to flatten their demand, rather shift as much demand as possible to the low-price period. We present PeakCharge, which includes a new peak-aware charging algorithm to optimize the use of energy storage in the presence of a peak demand surcharge, and use a closed-loop simulator to quantify its ability to flatten grid demand as the use of energy storage scales. We show that our system i) reduces upfront capital costs since it requires significantly less storage capacity per consumer than prior approaches, ii) increases energy storages ROI, since the surcharge mitigates free riding and maintains the incentive to use energy storage at scale, and iii) uses aggregate storage capacity within 18% of an optimal centralized system.

GreenCharge: Managing RenewableEnergy in Smart Buildings

Distributed generation (DG) uses many small on-site energy harvesting deployments at individual buildings to generate electricity. DG has the potential to make generation more efficient by reducing transmission and distribution losses, carbon emissions, and demand peaks. However, since renewables are intermittent and uncontrollable, buildings must still rely, in part, on the electric grid for power. While DG deployments today use net metering to offset costs and balance local supply and demand, scaling net metering for intermittent renewables to a large fraction of buildings is challenging. In this paper, we explore an alternative approach that combines market-based electricity pricing models with on-site renewables and modest energy storage (in the form of batteries) to incentivize DG. We propose a system architecture and optimization algorithm, called GreenCharge, to efficiently manage the renewable energy and storage to reduce a buildings electric bill. To determine when to charge and discharge the battery each day, the algorithm leverages prediction models for forecasting both future energy demand and future energy harvesting. We evaluate GreenCharge in simulation using a collection of real-world data sets, and compare with an oracle that has perfect knowledge of future energy demand/harvesting and a system that only leverages a battery to lower costs (without any renewables). We show that GreenCharges savings for a typical home today are near 20%, which are greater than the savings from using only net metering.

S-OSPF: A Traffic Engineering Solution for OSPF Based Best Effort Networks

Open Shortest Path First (OSPF) is one of the most widely used intra-domain routing protocol. It is well known that OSPF protocol does not provide flexibility in terms of packet forwarding to achieve any network optimization objective. Because of the high cost of network assets and commercial and competitive nature of Internet service provisioning, service providers are interested in performance optimization of their networks. This helps in reducing congestion hotspots and improving resource utilization across the network, which, in turn, results in an increased revenue collection. One way of achieving this is through Traffic Engineering. Currently traffic engineering is mostly done by using MPLS. But legacy networks running OSPF would need to be upgraded to MPLS. To achieve better resource utilization without upgrading OSPF network to MPLS is a challenge. In this paper we present a simple but effective algorithm, called Smart OSPF (S-OSPF) to provide traffic engineering solution in an OSPF based best effort network. We formulate an optimization problem based on the traffic demand to minimize the maximum link utilization in the network. Routing of the traffic demand is achieved using OSPF. We have simulated S-OSPF on real networks of two service providers. Simulation results show that S- OSPF based traffic engineering solution performance very closely follows the optimal solution.

Exploiting home automation protocols for load monitoring in smart buildings

Monitoring and controlling electrical loads is crucial for demand-side energy management in smart grids. Home automation (HA) protocols, such as X10 and Insteon, have provided programmatic load control for many years, and are being widely deployed in early smart grid field trials. While HA protocols include basic monitoring functions, extreme bandwidth limitations (<180bps) have prevented their use in load monitoring. In this paper, we highlight challenges in designing AutoMeter, a system for exploiting HA for accurate load monitoring at scale. We quantify Insteons limitations to query device status---once every 10 seconds to achieve less than 5% loss rate---and then evaluate techniques to disaggregate coarse HA data from fine-grained building-wide power data. In particular, our techniques learn switched load power using on-off-dim events, and tag fine-grained building-wide power data using readings from plug meters every 5 minutes.

Minimizing electricity costs by sharing energy in sustainable microgrids

Buildings account for over 75% of the electricity consumption in the United States. To reduce electricity usage and peak demand, many utilities are introducing market-based time-of-use (TOU) pricing models. In parallel, government programs that increase the fraction of renewable energy are incentivizing residential consumers to adopt on-site renewables and energy storage. Connecting on-site renewables and energy storage between homes forms a sustainable microgrid capable of generating, storing, and sharing electricity to balance local generation and consumption in residential areas. In this paper, we investigate how to minimize the costs of electricity from a utility for a microgrid under market-based TOU pricing models. In particular, we (i) present a system architecture for an energy-sharing microgrid; and (ii) develop optimal energy-sharing algorithms for homes within the microgrid. We conduct an extensive evaluation under two typical TOU pricing models that use data from more than 40 homes. Our results indicate that our system reduces the costs of Alternating Current (AC) electricity by 20%, even for homes with similar energy usage patterns.

Beyond MLU: An application-centric comparison of traffic engineering schemes

Traffic engineering (TE) has been long studied as a network optimization problem, but its impact on user-perceived application performance has received little attention. Our paper takes a first step to address this disparity. Using real traffic matrices and topologies from three ISPs, we conduct very large-scale experiments simulating ISP traffic as an aggregate of a large number of TCP flows. Our application-centric, empirical approach yields two rather unexpected findings. First, link utilization metrics, and MLU in particular, are poor predictors of application performance. Despite significant differences in MLU, all TE schemes and even a static shortest-path routing scheme achieve nearly identical application performance. Second, application adaptation in the form of location diversity, i.e., the ability to download content from multiple potential locations, significantly improves the capacity achieved by all schemes. Even the ability to download from just 2–4 locations enables all TE schemes to achieve near-optimal capacity, and even static routing to be within 30% of optimal. Our findings call into question the value of TE as practiced today, and compel us to significantly rethink the TE problem in the light of application adaptation.