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Showing papers by "Shahin Nazarian published in 2015"


Proceedings ArticleDOI
16 Jul 2015
TL;DR: Simulation results over real-world PV power generation and load power consumption profiles demonstrate that the proposed reinforcement learning-based storage control algorithm can achieve up to 59.8% improvement in energy cost reduction.
Abstract: Incorporating residential-level photovoltaic energy generation and energy storage systems have proved useful in utilizing renewable power and reducing electric bills for the residential energy consumer. This is particular true under dynamic energy prices, where consumers can use PV-based generation and controllable storage modules for peak shaving on their power demand profile from the grid. In general, accurate PV power generation and load power consumption predictions and accurate system modeling are required for the storage control algorithm in most previous works. In this work, the reinforcement learning technique is adopted for deriving the optimal control policy for the residential energy storage module, which does not depend on accurate predictions of future PV power generation and/or load power consumption results and only requires partial knowledge of system modeling. In order to achieve higher convergence rate and higher performance in non-Markovian environment, we employ the TD(Λ)-learning algorithm to derive the optimal energy storage system control policy, and carefully define the state and action spaces, and reward function in the TD(Λ)-learning algorithm such that the objective of the reinforcement learning algorithm coincides with our goal of electric bill minimization for the residential consumer. Simulation results over real-world PV power generation and load power consumption profiles demonstrate that the proposed reinforcement learning-based storage control algorithm can achieve up to 59.8% improvement in energy cost reduction.

42 citations


Proceedings ArticleDOI
07 Jun 2015
TL;DR: Experimental results show that the proposed optimal PEV charging algorithm minimizes the combination of electricity cost and battery aging cost in the RS provisioning power market.
Abstract: Plug-in electric vehicles (PEVs) are considered the key to reducing the fossil fuel consumption and an important part of the smart grid. The plug-in electric vehicle-to-grid (V2G) technology in the smart grid infrastructure enables energy flow from PEV batteries to the power grid so that the grid stability is enhanced and the peak power demand is shaped. PEV owners will also benefit from V2G technology as they will be able to reduce energy cost through proper PEV charging and discharging scheduling. Moreover, power regulation service (RS) reserves have been playing an increasingly important role in modern power markets. It has been shown that by providing RS reserves, the power grid achieves a better match between energy supply and demand in presence of volatile and intermittent renewable energy generation. This paper addresses the problem of PEV charging under dynamic energy pricing, properly taking into account the degradation of battery state-of-health (SoH) during V2G operations as well as RS provisioning. An overall optimization throughout the whole parking period is proposed for the PEV and an adaptive control framework is presented to dynamically update the optimal charging/discharging decision at each time slot to mitigate the effect of RS tracking error. Experimental results show that the proposed optimal PEV charging algorithm minimizes the combination of electricity cost and battery aging cost in the RS provisioning power market.

18 citations


Proceedings ArticleDOI
12 Mar 2015
TL;DR: A negotiation-based iterative approach has been proposed for joint residential task scheduling and energy storage control that is inspired by the state-of-the-art Field-Programmable Gate Array (FPGA) routing algorithms, and achieves up to 64.22% in the total energy cost reduction compared with the baseline methods.
Abstract: Dynamic energy pricing is a promising technique in the Smart Grid to alleviate the mismatch between electricity generation and consumption. Energy consumers are incentivized to shape their power demands, or more specifically, schedule their electricity-consuming applications (tasks) more prudently to minimize their electric bills. This has become a particularly interesting problem with the availability of residential photovoltaic (PV) power generation facilities and controllable energy storage systems. This paper addresses the problem of joint task scheduling and energy storage control for energy consumers with PV and energy storage facilities, in order to minimize the electricity bill. A general type of dynamic pricing scenario is assumed where the energy price is both time-of-use and power-dependent, and various energy loss components are considered including power dissipation in the power conversion circuitries as well as the rate capacity effect in the storage system. A negotiation-based iterative approach has been proposed for joint residential task scheduling and energy storage control that is inspired by the state-of-the-art Field-Programmable Gate Array (FPGA) routing algorithms. In each iteration, it rips-up and re-schedules all tasks under a fixed storage control scheme, and then derives a new charging/discharging scheme for the energy storage based on the latest task scheduling. The concept of congestion is introduced to dynamically adjust the schedule of each task based on the historical results as well as the current scheduling status, and a near-optimal storage control algorithm is effectively implemented by solving convex optimization problem(s) with polynomial time complexity. Experimental results demonstrate the proposed algorithm achieves up to 64.22% in the total energy cost reduction compared with the baseline methods.

18 citations


Proceedings ArticleDOI
09 Mar 2015
TL;DR: It is demonstrated that, compared to Dual-VT, GLB is a more suitable technique for the advanced 7nm FinFET technology due to its capability of delivering a finer-grained trade-off between the leakage power and circuit speed, not to mention the lower manufacturing cost.
Abstract: With the aggressive downscaling of the process technologies and importance of battery-powered systems, reducing leakage power consumption has become one of the most crucial design challenges for IC designers. This paper presents a device-circuit cross-layer framework to utilize fine-grained gate-length biased FinFETs for circuit leakage power reduction in the near- and super-threshold operation regimes. The impacts of Gate-Length Biasing (GLB) on circuit speed and leakage power are first studied using one of the most advanced technology nodes — a 7nm FinFET technology. Then multiple standard cell libraries using different leakage reduction techniques, such as GLB and Dual-Fj-, are built in multiple operating regimes at this technology node. It is demonstrated that, compared to Dual-Fj-, GLB is a more suitable technique for the advanced 7nm FinFET technology due to its capability of delivering a finer-grained trade-off between the leakage power and circuit speed, not to mention the lower manufacturing cost. The circuit synthesis results of a variety of ISCAS benchmark circuits using the presented GLB 7nm FinFET cell libraries show up to 70% leakage improvement with zero degradation in circuit speed in the near- and super-threshold regimes, respectively, compared to the standard 7nm FinFET cell library.

10 citations


Proceedings ArticleDOI
22 Jul 2015
TL;DR: This work proposes a reconfigurable power delivery network architecture, comprised of a small number of DC-DC converters, a switch network and an online controller, to realize fine-grained DVS in large-area OLED display panels, which consistently achieves high power conversion efficiency and significant energy saving while preserving the image quality.
Abstract: Dynamic voltage scaling (DVS) has proven effective in minimizing the power consumption of OLED displays, resulting only in minimal image distortion. This technique has been extended to perform zone-specific DVS by dividing the panel area into zones and applying independent DVS to each zone based on the displayed content. The application of the latter technique to large-area OLED displays has not been done in part due to a high overhead of its dedicated DC-DC converter for each zone and low conversion efficiency when the load current of each converter lies outside the desirable range. To address this issue, this work proposes a reconfigurable power delivery network architecture, comprised of a small number of DC-DC converters, a switch network and an online controller, to realize fine-grained (zone-specific) DVS in large-area OLED display panels. The proposed framework consistently achieves high power conversion efficiency and significant energy saving while preserving the image quality. Experimental results demonstrate that up to 36% power savings can be achieved in a 65″ 4K Ultra high-definition OLED display by using the proposed framework.

8 citations


Proceedings ArticleDOI
20 May 2015
TL;DR: A power density analysis is presented for 7nm FinFET technology node based on both shorted-gate (SG) and independent-Gate (IG) standard cells operating in multiple supply voltage regimes and shows that the back-gate signal enables a better control of power consumption for independent-gate FinFets.
Abstract: In this paper, a power density analysis is presented for 7nm FinFET technology node based on both shorted-gate (SG) and independent-gate (IG) standard cells operating in multiple supply voltage regimes. A Liberty-formatted standard cell library is established by selecting the appropriate number of fins for the pull-up and pull-down networks of each logic cell. The layout of both shorted-gate and independent-gate standard cells are then characterized according to lambda-based layout design rules for FinFET devices. Finally, the power density of 7nm FinFET technology node is analyzed and compared with the 45 nm CMOS technology node for different circuits. Experimental result shows that the power density of each 7nm FinFET circuit is 3-20 times larger than that of 45nm CMOS circuit under the spacer-defined technology. Experimental result also shows that the back-gate signal enables a better control of power consumption for independent-gate FinFETs.

6 citations


Proceedings ArticleDOI
01 Oct 2015
TL;DR: In this paper, the authors presented a device and circuit (8T SRAM) co-simulation work based on junctionless gate-all-around (JL-GAA) FETs.
Abstract: Gate-all-around (GAA) FETs is proposed as a choice for deeply scaled MOSFETs beyond the 10 nm technology node. In this paper, we present a device and circuit (8T SRAM) co-simulation work based on Junctionless-GAA (JL-GAA) FETs. The same doping concentration level in channel and source/drain can mitigate fabrication complexity and process variability. The 8T SRAM monte carlo simulation results considering process variations shows JL-GAA FETs can reliably operate at low supply voltage.

4 citations