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  1. We introduce and study the online pause and resume problem. In this problem, a player attempts to find the k lowest (alternatively, highest) prices in a sequence of fixed length T, which is revealed sequentially. At each time step, the player is presented with a price and decides whether to accept or reject it. The player incurs aswitching cost whenever their decision changes in consecutive time steps, i.e., whenever they pause or resume purchasing. This online problem is motivated by the goal of carbon-aware load shifting, where a workload may be paused during periods of high carbon intensity and resumed during periods of low carbon intensity and incurs a cost when saving or restoring its state. It has strong connections to existing problems studied in the literature on online optimization, though it introduces unique technical challenges that prevent the direct application of existing algorithms. Extending prior work on threshold-based algorithms, we introducedouble-threshold algorithms for both the minimization and maximization variants of this problem. We further show that the competitive ratios achieved by these algorithms are the best achievable by any deterministic online algorithm. Finally, we empirically validate our proposed algorithm through case studies on the application of carbon-aware load shifting using real carbon trace data and existing baseline algorithms.

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    Free, publicly-accessible full text available December 7, 2024
  2. The impact of human activity on the climate is a major global challenge that affects human well-being. Buildings are a major source of energy consumption and carbon emissions worldwide, especially in advanced economies such as the United States. As a result, making grids and buildings sustainable by reducing their carbon emissions is emerging as an important step toward societal decarbonization and improving overall human well-being. While prior work on demand response methods in power grids and buildings has targeted peak shaving and price arbitrage in response to price signals, it has not explicitly targeted carbon emission reductions. In this paper, we analyze the flexibility of building loads to quantify the upper limit on their potential to reduce carbon emissions, assuming perfect knowledge of future demand and carbon intensity. Our analysis leverages real-world demand patterns from 1000+ buildings and carbon-intensity traces from multiple regions. It shows that by manipulating the demand patterns of electric vehicles, heating, ventilation, and cooling (HVAC) systems, and battery storage, we can reduce carbon emissions by 26.93% on average and by 54.90% at maximum. Our work advances the understanding of sustainable infrastructure by highlighting the potential for infrastructure design and interventions to significantly reduce carbon footprints, benefiting human well-being. 
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    Free, publicly-accessible full text available November 15, 2024
  3. Residential heating, primarily powered by natural gas, accounts for a significant portion of residential sector energy use and carbon emissions in many parts of the world. Hence, there is a push towards decarbonizing residential heating by transitioning to energyefficient heat pumps powered by an increasingly greener and less carbon-intensive electric grid. However, such a transition will add additional load to the electric grid triggering infrastructure upgrades, and subsequently erode the customer base using the gas distribution network. Utilities want to guide these transition efforts to ensure a phased decommissioning of the gas network and deferred electric grid infrastructure upgrades while achieving carbon reduction goals. To facilitate such a transition, we present a network-aware optimization framework for decarbonizing residential heating at city scale with an objective to maximize carbon reduction under budgetary constraints. Our approach operates on a graph representation of the gas network topology to compute the cost of transitioning and select neighborhoods for transition. We further extend our approach to explicitly incorporate equity and ensure an equitable distribution of benefits across different socioeconomic groups. We apply our framework to a city in the New England region of the U.S., using real-world gas usage, electric usage, and grid infrastructure data. We show that our networkaware strategy achieves 55% higher carbon reductions than prior network-oblivious work under the same budget. Our equity-aware strategy achieves an equitable outcome while preserving the carbon reduction benefits of the network-aware strategy. 
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  4. Major innovations in computing have been driven by scaling up computing infrastructure, while aggressively optimizing operating costs. The result is a network of worldwide datacenters that consume a large amount of energy, mostly in an energy-efficient manner. Since the electric grid powering these datacenters provided a simple and opaque abstraction of an unlimited and reliable power supply, the computing industry remained largely oblivious to the carbon intensity of the electricity it uses. Much like the rest of the society, it generally treated the carbon intensity ofthe electricity as constant, which was mostly true fora fossil fuel-driven grid. As a result, the cost-driven objective of increasing energy-efficiency - by doing more work per unit of energy - has generally been viewed as the most carbon-efficient approach. However, as the electric grid is increasingly powered by clean energy and is exposing its time-varying carbon intensity, the most energy-efficient operation is no longer necessarily the most carbon-efficient operation. There has been a recent focus on exploiting the flexibility of computing's workloads-along temporal, spatial,and resource dimensions-to reduce carbon emissions,which comes at the cost ofeither perfor- mance or energy efficiency. In this paper, we discuss the trade-offs between energy efficiency and carbon efficiency in exploiting com- puting's flexibility and show that blindly optimizing for energy efficiency is not always the right approach. 
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  5. The online knapsack problem is a classic online resource allocation problem in networking and operations research. Its basic version studies how to pack online arriving items of different sizes and values into a capacity-limited knapsack. In this paper, we study a general version that includes item departures, while also considering multiple knapsacks and multi-dimensional item sizes. We design a threshold-based online algorithm and prove that the algorithm can achieve order-optimal competitive ratios. Beyond worst-case performance guarantees, we also aim to achieve near-optimal average performance under typical instances. Towards this goal, we propose a data-driven online algorithm that learns within a policy-class that guarantees a worst-case performance bound. In trace-driven experiments, we show that our data-driven algorithm outperforms other benchmark algorithms in an application of online knapsack to job scheduling for cloud computing. 
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