Showing papers by "Shahin Nazarian published in 2016"

Optimal co-scheduling of HVAC control and battery management for energy-efficient buildings considering state-of-health degradation

Abstract: The heating, ventilation and air conditioning (HVAC) system accounts for half of the energy consumption of a typical building. Additionally, the need for HVAC changes over hours and days as does the electric energy price. Level of comfort of the building occupants is, however, a primary concern, which tends to overwrite pricing. Dynamic HVAC control under a dynamic energy pricing model while meeting an acceptable level of occupants' comfort is thus critical to achieving energy efficiency in buildings in a sustainable manner. Finally, there is the possibility that the building is equipped with some renewable source of power such as solar panels mounted on the rooftop. The presence of a battery energy storage system in a target building would enable peak power shaving by adopting a suitable charge and discharge schedule for the battery, while simultaneously meeting building energy efficiency and user satisfaction. Achieving this goal requires detailed information (or predictions) about the amount of local power generation from the renewable source plus the power consumption load of the building. This paper addresses the coscheduling problem of HVAC control and battery management to achieve energy-efficient buildings, while also accounting for the degradation of the battery state-of-health during charging and discharging operations (which in turn determines the amortized cost of owning and utilizing a battery storage system)aącc A time-of-use dynamic pricing scenario is assumed and various energy loss components are considered including power dissipation in the power conversion circuitry as well as the rate capacity effect in the battery. A global optimization framework targeting the entire billing cycle is presented and an adaptive co-scheduling algorithm is provided to dynamically update the optimal HVAC air flow control and the battery charging/discharging decision in each time slot during the billing cycle to mitigate the prediction error of unknown parameters. Experimental results show that the proposed algorithm achieves up to 15% in the total electric utility cost reduction compared with some baseline methods.

An efficient timing analysis model for 6T FinFET SRAM using current-based method

Abstract: Accurate timing analysis is a critical step in the design of VLSI circuits. In addition, nanoscale FinFET devices are emerging as the transistor of choice in 32nm CMOS technologies and beyond. This is due to their more effective channel control, higher ON/OFF current ratios, and lower energy consumption. In this paper, an efficient Current Source Model (CSM) is presented to calculate the output waveform as well as the read/write delay of 6T FinFET SRAM cells accounting for noisy waveform at each voltage node. In this model, the non-linear analytical methods and low-dimensional CSM lookup tables (LUTs) are combined to simultaneously achieve high modeling accuracy and time/space efficiency. Experimental data shows that our proposed framework not only provides accurate results in timing analysis, but also can capture the effect of arbitrary voltage noise.

Negotiation-based resource provisioning and task scheduling algorithm for cloud systems

Abstract: Cloud computing has drawn significant attention from both academia and industry as an emerging computing paradigm where data, applications, or processing power are provided as services through the Internet. Cloud computing extends the existing computing infrastructure owned by the cloud service providers (CSPs) to achieve the economies of scale through virtualization and aggregated computing resources. End users, on the other hand, can reach these services through an elastic utility computing environment with minimal upfront investment. Nevertheless, pervasive use of cloud computing and the resulting rise in the number of data centers have brought forth concerns about energy consumption and carbon emission. Therefore, this paper addresses the problem of resource provisioning and task scheduling on a cloud platform under given service level agreements, in order to minimize the electric bills and maximize the profitability for the CSP. User task graphs and dependencies are randomly generated, whereas user requests for CPU and memory resources are extracted from the Google cluster trace. A general type of dynamic pricing scenario is assumed where the energy price is both time-of-use and total power consumption-dependent. A negotiation-based iterative approach has been proposed for the resource provisioning and task scheduling that is inspired by a routing algorithm. More specifically, in each iteration, decisions made in the previous iteration are ripped-up and re-decided, while a congestion model is introduced to dynamically adjust the resource provisioning decisions and the schedule of each task based on the historical results as well as the current state of affairs. Experimental results demonstrate that the proposed algorithm achieves up to 63% improvement in the total electrical energy bill of an exemplary data center compared to the baseline.

Standard cell library based layout characterization and power analysis for 10nm gate-all-around (GAA) transistors

Abstract: Gate-all-around (GAA) nanowire transistor is promising for continuing scaling down the feature size of transistors beyond sub-10nm because it provides the gate with better controllability over the channel by wrapping around. In this paper, the device model for 10nm gate length conventional GAA (C-GAA) and junctionless GAA (JL-GAA) are extracted based on the TCAD simulation. The layout design of GAA transistors are characterized for different sizing methods. Liberty-formatted standard cell libraries are constructed by appropriately sizing pull-up and pull-down networks of each logic cell. Based on the library, power densities of 10nm technology node C-GAA and JL-GAA are analyzed under benchmark circuits in comparing with 7nm FinFET technology. Experimental results show that the vertical C-GAA transistor can achieve 28% area reduction and the horizontal C-GAA transistor can reduce 29% power consumption comparing with other C-GAA geometries. The power density of JL-GAA circuits can reach above the limit of air cooling and thermal management techniques are needed for JL-GAA circuits.

Profit maximization algorithms for utility companies in an oligopolistic energy market with dynamic prices and intelligent users

Abstract: Dynamic energy pricing provides a promising solution for the utility companies to incentivize energy users to perform demand side management in order to minimize their electric bills. Moreover, the emerging decentralized smart grid, which is a likely infrastructure scenario for future electrical power networks, allows energy consumers to select their energy provider from among multiple utility companies in any billing period. This paper thus starts by considering an oligopolistic energy market with multiple non-cooperative (competitive) utility companies, and addresses the problem of determining dynamic energy prices for every utility company in this market based on a modified Bertrand Competition Model of user behaviors. Two methods of dynamic energy pricing are proposed for a utility company to maximize its total profit. The first method finds the greatest lower bound on the total profit that can be achieved by the utility company, whereas the second method finds the best response of a utility company to dynamic pricing policies that the other companies have adopted in previous billing periods. To exploit the advantages of each method while compensating their shortcomings, an adaptive dynamic pricing policy is proposed based on a machine learning technique, which finds a good balance between invocations of the two aforesaid methods. Experimental results show that the adaptive policy results in consistently high profit for the utility company no matter what policies are employed by the other companies.