Summary Blockchain technologies are expected to make a significant impact on a variety of industries. However, one issue holding them back is their limited transaction throughput, especially compared to established solutions such as distributed database systems. In this paper, we rearchitect a modern permissioned blockchain system, Hyperledger Fabric, to increase transaction throughput from 3000 to 20 000 transactions per second. We focus on performance bottlenecks beyond the consensus mechanism, and we propose architectural changes that reduce computation and I/O overhead during transaction ordering and validation to greatly improve throughput. Notably, our optimizations are fully plug‐and‐play and do not require any interface changes to Hyperledger Fabric.

Understanding the energy usage of buildings is crucial for policymaking, energy planning, and achieving sustainable development. Unfortunately, instrumenting buildings to collect energy usage data is difficult and all publicly available datasets typically include only a few hundred homes within a region. Due to their relatively small size, these datasets provide limited insight and are insufficient for analyses that require a larger representation, such as an entire city or town. In recent years, utility companies have installed advanced electric and gas meters, i.e., "smart meters" that enable energy data collection on a massive scale. In this paper, we analyze such a dataset from a utility company that includes energy data from 14,836 smart meters covering a small city. We conduct a wideranging analysis of the city's gas and electric data to gain insights into the energy consumption of both individual homes and the city as a whole. In doing so, we demonstrate how city-scale smart meter datasets can answer a variety of questions on building energy consumption, such as the impact of weather on energy usage, the correlation between the size and age of a building and its energy usage, the impact of increasing levels of renewable penetration, etc. For example, we show that extreme weather events significantly increase energy usage, e.g., by 36% and 11.5% on hot summer and cold winter days, respectively. As another example, we observe that 700 homes are highly energy inefficient as its energy demand variability is twice that of the aggregate grid demand. Finally, we study the impact of increasing level of renewable integration in homes and show that solar penetration rates higher than 20% of demand increases the risk of over-generation and may impact utility operations.

Infrastructure-as-a-Service (IaaS) cloud platforms rent resources, in the form of virtual machines (VMs), under a variety of contract terms that offer different levels of risk and cost. For example, users may acquire VMs in the spot market that are often cheap but entail significant risk, since their price varies over time based on market supply and demand and they may terminate at any time if the price rises too high. Currently, users must manage all the risks associated with using spot servers. As a result, conventional wisdom holds that spot servers are only appropriate for delay-tolerant batch applications. In this paper, we propose a derivative cloud platform, called SpotCheck, that transparently manages the risks associated with using spot servers for users.SpotCheck provides the illusion of an IaaS platform that offers always-available VMs on demand for a cost near that of spot servers, and supports all types of applications, including interactive ones. SpotCheck's design combines the use of nested VMs with live bounded-time migration and novel server pool management policies to maximize availability, while balancing risk and cost. We implement SpotCheck on Amazon's EC2 and show that it i) provides nested VMs to users that are 99.9989% available, ii) achieves nearly 5× cost savings compared to using equivalent types of ondemand VMs, and iii) eliminates any risk of losing VM state.

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Electric vehicles (EV) are rapidly increasing in popularity, which is signicantly increasing demand on the distribution infrastructure in the electric grid. This poses a serious problem for the grid, as most distribution transformers were installed during the pre-EV era, and thus were not sized to handle large loads from EVs. In parallel, smart grid technologies have emerged that actively regulate demand to prevent overloading the grid's infrastructure, in particular by optimizing the use of grid-scale energy storage. In this paper, we rst analyze the load on distribution transformers across a small city and study the potential impact of EVs as their penetration levels increase. Our real-world dataset includes the energy demand from 1,353 transformers and charging proles from 91 EVs over a 1 year period, and thus provides an accurate snapshot of the grid's current state, and allows us to examine the potential impact of increasing EV penetrations. We then evaluate the benets of using smart grid technologies, such as smart EV charging and energy storage, to mitigate the eects of increasing the EV-based load.

Electricity generation combined with its transmission and distribution form the majority of an electric utility's recurring operating costs. These costs are determined, not only by the aggregate energy generated, but also by the maximum instantaneous peak power demand required over time. Prior work proposes using energy storage devices to reduce these costs by periodically releasing energy to lower the electric grid's peak demand. However, prior work generally considers only a single storage technology employed at a single level of the electric grid's hierarchy. In this paper, we examine the efficacy of employing different combinations of storage technologies at different levels of the grid's distribution hierarchy. We present an optimization framework for modeling the primary characteristics that dictate the lifetime cost of many prominent energy storage technologies. Our framework captures the important tradeoffs in placing different technologies at different levels of the distribution hierarchy with the goal of minimizing a utility's operating costs. We evaluate our framework using real smart meter data from 5000 customers of a local electric utility. We show that by employing hybrid storage technologies at multiple levels of the distribution hierarchy, utilities can reduce their daily operating costs due to distributing electricity by up to 12%.

Reducing our reliance on carbon-intensive energy sources is vital for reducing the carbon footprint of the electric grid. Although the grid is seeing increasing deployments of clean, renewable sources of energy, a significant portion of the grid demand is still met using traditional carbon-intensive energy sources. In this paper, we study the problem of using energy storage deployed in the grid to reduce the grid's carbon emissions. While energy storage has previously been used for grid optimizations such as peak shaving and smoothing intermittent sources, our insight is to use distributed storage to enable utilities to reduce their reliance on their less efficient and most carbon-intensive power plants and thereby reduce their overall emission footprint. We formulate the problem of emissionaware scheduling of distributed energy storage as an optimization problem, and use a robust optimization approach that is well-suited for handling the uncertainty in load predictions, especially in the presence of intermittent renewables such as solar and wind. We evaluate our approach using a state of the art neural network load forecasting technique and real load traces from a distribution grid with 1,341 homes. Our results show a reduction of >0.5 million kg in annual carbon emissions -equivalent to a drop of 23.3% in our electric grid emissions.

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 article, 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, as it requires significantly less storage capacity per consumer; (ii) increases energy storage’s return on investment, as it mitigates free riding and maintains the incentive to use energy storage at large scales; and (iii) uses aggregate storage capacity within 31% of an optimal centralized approach. In addition, unlike variable rate pricing, we also show that flat-power pricing incentivizes the 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.