Peak Power Consumption Research Articles

Simultaneous Management of Peak-Power and Reliability in Heterogeneous Multicore Embedded Systems

Analysis of reliability, power, and performance at hardware and software levels due to heterogeneity is a crucial requirement for heterogeneous multicore embedded systems. Escalating power densities have led to thermal issues for heterogeneous multicore embedded systems. This paper proposes a peak-power-aware reliability management scheme to meet power constraints through distributing power density on the whole chip such that reliability targets are satisfied. In this paper, we consider peak power consumption as a system-level power constraint to prevent system failure. To balance the power consumption, we also employ a Dynamic Frequency Scaling (DFS) method to further reduce peak power consumption and satisfy thermal constraints on the chip. We illustrate the benefits of our scheme by comparing it with state-of-the-art schemes, resulting in average in 26.5 percent less peak power consumption (up to 54.3 percent).

Application of Predictive Control in Scheduling of Domestic Appliances

In this work, an algorithm for the scheduling of household appliances to reduce the energy cost and the peak-power consumption is proposed. The system architecture of a home energy management system (HEMS) is presented to operate the appliances. The dynamics of thermal and non-thermal appliances is represented into state-space model to formulate the scheduling task into a mixed-integer-linear-programming (MILP) optimization problem. Model predictive control (MPC) strategy is used to operate the appliances in real-time. The HEMS schedules the appliances in dynamic manner without any a priori knowledge of the load-consumption pattern. At the same time, the HEMS responds to the real-time electricity market and the external environmental conditions (solar radiation, ambient temperature, etc.). Simulation results exhibit the benefits of the proposed HEMS by showing the reduction of up to 70% in electricity cost and up to 57% in peak power consumption.

MANFIS based SMART home energy management system to support SMART grid

With the emergence of the smart grid, the customers get the chance to involve in demand-side management and to combine renewable energy sources. Renewable energy sources integration in smart grid is difficult due to intermittent and fluctuating nature of Renewable energy. The variation in renewable energy generation is smooth out by utilizing the Energy Storage System in a smart house. This paper introduces the Smart Home Energy Management System using Multi-output Adaptive neuro-fuzzy inference system for efficient management of Energy Storage System, scheduled appliances and to integrate Renewable energy. The controller can interrupt or postpone scheduled appliances and trading surplus energy between consumers thereby reducing reverse power flow and electricity cost. The proposed system is tested with daily data of wind speed, temperature, insolation, uncontrollable and controllable appliances power and electrical energy price as inputs to validate the results. The output of the control system decides how to handle energy production, consumption, and scheduling of the appliance. The result shows that the electricity cost reduced by 57.62%, peak power consumption is reduced by 44.4% and peak-to-average ratio is reduced by 73.6% after adopting the proposed strategy.

Conservation Voltage Reduction Case Study

A Conservation Voltage Reduction (CVR) is an efficient method to manage the load demand in distribution power systems. This paper proposes the utilization of the CVR method on Washington EMC’s power system and Georgia Transmission Corporation facilities. The utility’s peak power consumption is reduced to determine the figures of wattage and money saved. The CVR technique is implemented within the ANSI standards and with an efficient CVR, factor to maintain the efficient performance of the system and satisfy the consumers’ needs. The principle of operation, concepts of modeling, and substation CVR management are illustrated. The CVR methodology including utility circuit, smart meters’ data, management, savings, and analysis is presented. Then, experimental CVR scenarios, smart meters’ experimental CVR data, CVR steady-state analysis and experimental CVR management using SCADA are investigated and analyzed. After that, a CVR dynamic prediction model is established using the Neural Network (NN) and validated by means of comparisons with actual data and the Gaussian model. Power saving, power demand, yearly money-saving real data from EMC utility, and real temperature data from NOAA is used to create the CVR dynamic model with a minimum average error percentage of 0.22 % w.r.t the real-data. Finally, a SCADA system operation is discussed along with the potential future improvements and research directions.

Spectral Flux-Based Convolutional Neural Network Architecture for Speech Source Localization and Its Real-Time Implementation.

In this article, we present a real-time convolutional neural network (CNN)-based Speech source localization (SSL) algorithm that is robust to realistic background acoustic conditions (noise and reverberation). We have implemented and tested the proposed method on a prototype (Raspberry Pi) for real-time operation. We have used the combination of the imaginary-real coefficients of the short-time Fourier transform (STFT) and Spectral Flux (SF) with delay-and-sum (DAS) beamforming as the input feature. We have trained the CNN model using noisy speech recordings collected from different rooms and inference on an unseen room. We provide quantitative comparison with five other previously published SSL algorithms under several realistic noisy conditions, and show significant improvements by incorporating the Spectral Flux (SF) with beamforming as an additional feature to learn temporal variation in speech spectra. We perform real-time inferencing of our CNN model on the prototyped platform with low latency (21 milliseconds (ms) per frame with a frame length of 30 ms) and high accuracy (i.e. 89.68% under Babble noise condition at 5dB SNR). Lastly, we provide a detailed explanation of real-time implementation and on-device performance (including peak power consumption metrics) that sets this work apart from previously published works. This work has several notable implications for improving the audio-processing algorithms for portable battery-operated Smart loudspeakers and hearing improvement (HI) devices.

Line Balancing and Sequencing for Peak Power Minimization

Abstract In the past years, environmental awareness started to bring new production paradigms based on energy efficiency. If it is possible to improve energy efficiency of existing production systems, it should be even more profitable to consider this objective at the design stage. In the context of Paced Production Lines, and given power requirements for operations, it becomes possible to assign more efficiently these operations to stations while respecting other constraints such as maximum takt time and number of workstations. The repetitive nature of paced lines implies that misconceptions implying a high peak power consumption will see this peak power repeated over and over without having large possibilities to correct it. In order to tackle peak power minimization objectives, this implies to consider sequencing of operations in addition to their assignment to workstation which is not classical in line balancing. In this paper, the problem under study is presented with a new specific feature that allows to consider semi-active sequence of operations at each station. In order to address large scale instances, a first metaheuristic approach is implemented and evaluated on an extended dataset from the literature. Results show that it is possible to improve energy efficiency at the design stage of production systems.

Model Predictive Control with Binary Quadratic Programming for the Scheduled Operation of Domestic Refrigerators

The rapid proliferation of the ‘Internet of Things’ (IoT) now affords the opportunity to schedule the operation of widely distributed domestic refrigerator and freezers to collectively improve energy efficiency and reduce peak power consumption on the electrical grid. To accomplish this, the paper proposes the real-time estimation of the thermal mass of each refrigerator in a network using on-line parameter identification, and the co-ordinated (ON-OFF) scheduling of the refrigerator compressors to maintain their respective temperatures within specified hysteresis bands commensurate with accommodating food safety standards. A custom model predictive control (MPC) scheme is devised using binary quadratic programming to realize the scheduling methodology which is implemented through IoT hardware (based on a NodeMCU). Benefits afforded by the proposed scheme are investigated through experimental trials which show that the co-ordinated operation of domestic refrigerators can i) reduce the peak power consumption as seen from the perspective of the electrical power grid (i.e., peak load levelling), ii) can adaptively control the temperature hysteresis band of individual refrigerators to increase operational efficiency, and iii) contribute to a widely distributed aggregated load shed for demand side response purposes in order to aid grid stability. Importantly, the number of compressor starts per hour for each refrigerator is also bounded as an inherent design feature of the algorithm so as not to operationally overstress the compressors and reduce their lifetime. Experimental trials show that such co-ordinated operation of refrigerators can reduce energy consumption by ~30% whilst also providing peak load levelling, thereby affording benefits to both individual consumers as well as electrical network suppliers.

An Economical Energy Management Strategy for Viable Microgrid Modes

In the last couple of decades, numerous energy management strategies have been devised to mitigate the effects of greenhouse gas emission, hence introducing the concept of microgrids. In a microgrid, distributed energy generators are used. Microgrid enables a point which ameliorates in exchanging power with the main grid during different times of day. Based on the system constraints, in this work, we aim to efficiently minimize the operating cost of the microgrid and shave the power consumption peaks. For this purpose, we introduce an improved binary bat (iBBat) algorithm which helps to schedule the load demand of smart homes and energy generation from distributed generator of microgrid to the load demand and supply. The proposed energy management algorithm is applied to both grid-connected and islanded modes of the microgrid. The constraints imposed on the algorithm ensure that the load of electricity consumer does not escalate during peak hours. The simulation results are compared with BBat and binary flower pollination algorithm, which validate that the iBBat reflects substantial reduction in operating cost of microgrid. Moreover, results also show a phenomenal reduction in the peak-to-average ratio of load demand from main the main grid.

Sensitivity analysis of a simplified office building

The increase of thermal discomfort and the associated energy demand for the cooling of buildings further exacerbate the ongoing challenges for the design of new buildings and the adaptation measures of the current building stock to climate change. Dynamic, detailed and tailored building simulation methodologies are necessary to better understand and quantify the energy demand of buildings and consequences for supporting energy networks. The aim of this is to analyze the sensitivity of peak electricity demand for the provision of space cooling to different office building model parameters. In the research, two sensitivity methods, Morris Elementary Effect and Sobol, are used to evaluate the sensitivity of eight input model parameters. Relative to peak power consumption for cooling, the most important factor is the cooling set-point, followed by ventilation rate and internal heat gains, which are estimated to account for 38%, 26% and 25% of the variation. Regarding the annual cooling demand for the HVAC systems, these factors account for 35%, 33% and 58%, respectively.

The effect of server energy proportionality on data center power oversubscription

Modern data centers improve resource utilization with power oversubscription. The power hierarchy in a data center is oversubscribed by installing more servers than allowed by the power budget based on server peak power consumption. Power oversubscription is possible due to the statistically low likelihood of simultaneous peak power operation of multiple servers. As future servers become more energy proportional, the opportunity for greater power oversubscription increases. The challenge is to quantify the level of oversubscription that can be attained. In this paper, we quantify the level of oversubscription possible for a given acceptable probability of power overload for servers characterized by energy proportionality metric and workload distribution. We develop a theoretical framework to characterize and predict the relationship between server energy proportionality and power oversubscription. We verify our framework through an extensive empirical study using publicly available SPECpower benchmark data for over 500 server models and publicly available Google cluster utilization data. Using our framework, a data center operator can predict the additional power oversubscription possible when replacing existing servers with a newer model of more energy proportional servers.

Assessment and data analysis of beneficial implementation of cogeneration modules at mining enterprises to minimize negative influence on the environment

This article analyses the negative influence on the environment exerted during coal mining and draining-out gases from coal deposits. A large quantity of side gases can be recycled into useful energy or warmth. Co-generation stations seem to be a perspective solution for recycling these wastes with minimal environmental casualties. This research describes and assesses an operation result based on the example of one co-generation station. The data for the analysis have been collected during two years. The data analysis reveals that during a two-year period of co-generation station maintenance, a mining enterprise has obtained benefits from CH4 utilization. The enterprise was able to cover its own peak power consumption (10 MW average) and even earn money delivering generated energy into the city network due to correct planning and efficient gas burning. Moreover, data analysis proves that integration of such cogeneration modules allows making networks with distributed generation (DG) sources more efficient and eco-friendlier because of utilization of side-gases from different technological processes even in small amounts

Bitcoin mining: A global review of energy and power demand

After its introduction in 2008, increasing Bitcoin prices and a booming number of other cryptocurrencies lead to a growing discussion of how much energy is consumed during the production of these currencies. Being the most expensive and the most popular cryptocurrency, both the business world and the research community have started to question the energy intensity of Bitcoin mining. This paper only focuses on computational power demand during the proof-of-work process rather than estimating the whole energy intensity of mining. We make use of 160GB of Bitcoin blockchain data to estimate the energy consumption and power demand of Bitcoin mining. We considered the performance of 269 different hardware models (CPU, GPU, FPGA, and ASIC). For estimations, we defined two metrics, namely; minimum consumption and maximum consumption. The targeted time span for the analysis was from 3 January 2009 to 5 June 2018. We show that the historical peak of power consumption of Bitcoin mining took place during the bi-weekly period commencing on 18 December 2017 with a demand of between 1.3 and 14.8 GW. This maximum demand figure was between the installed capacities of Finland (∼16 GW) and Denmark (∼14 GW). We also show that, during June 2018, energy consumption of Bitcoin mining from difficulty recalculation was between 15.47 and 50.24 TWh per year.

A low-cost concurrent TSV test architecture with lossless test output compression scheme.

As the traditional IC design migrates to three-dimensional integrated circuits (3D-ICs) design, new challenges need to be considered carefully to solve its reliability and yield issues. 3D-ICs using through-silicon-vias (TSVs) can have latent defects such as resistive open and bridge defects, which are caused by the thermal stress during the fabrication process. These latent defects lead to the deterioration of the electrical performance of TSVs caused by an undesired increase in the resistance-capacitance (RC) delay. For this reason, various post-bond test methodologies have been studied to improve the reliability of 3D-ICs. Cost reduction in these TSV test architectures is also currently being studied by decreasing various factors such as hardware overhead, test time, and the peak current consumption. Usually, a single test-clock-period is required to determine whether the test result contains the defective TSV. When the test result of any TSVs fails, we use another single test-clock-period to classify its defect type. In this paper, we propose a new TSV test architecture to transfer the combined test output of the test result and the specific defect type to the pad during the single test-clock-period. Our proposed test architecture also provides a reliable block-based concurrent testing to optimize the test time by dividing the die into concurrent blocks. The experimental results showed that our proposed test architecture could reduce the test time and the hardware overhead substantially by ensuring that the reasonable peak power consumption for mass production was reasonable without the test quality being adversely affected.

Demand response-integrated economic dispatch incorporating renewable energy sources using ameliorated dragonfly algorithm

This paper presents an integrated demand response (DR) and dynamic economic dispatch (DED) model consisting of renewable energy sources (RESs) and shunt FACTS (flexible AC transmission systems) devices with focus on reducing thermal energy consumption. RESs are environment friendly and can be located close to the load centers, which helps in reducing T&D losses as well as harmful emissions. DR helps in reducing peak power consumption and generation cost of thermal power plants. Shunt FACTS device, static synchronous compensator (STATCOM), helps in improving voltage profile of the network. Analyses are carried out using the proposed ameliorated dragonfly algorithm (ADFA). The efficiency of developed ADFA is proved by comparing the obtained results with many other methods. Static economic dispatch is carried out on 6-, 17- and 40-thermal generator test systems using ADFA, and the results are compared with other existing methods available in the literature. Further, DED is carried out on 6- and 17-thermal generator test systems. Later, a DED is performed on a real-time 17-thermal generator (South Indian 86-bus system) system for four seasons. Solar irradiance and wind speed are modeled using beta distribution and Weibull distribution functions by collecting the historical data of the test system. The developed demand response program is implemented on a real-time 17 thermal unit and 86-bus test system located in Tamil Nadu, southern part of India. The developed DR model alters time-independent loads so that burden on generating stations during the peak load is minimized. The effect of DR, RES and STATCOM in the test system is studied individually. Further, a comparative analysis considering DR, RES and STATCOM together is presented. The results obtained are promising, and it can be interpreted that by using the developed model there will be improvement in the penetration levels of RES which in turn reduces the demand for thermal energy.

Optimal Home Energy Management System With Demand Charge Tariff and Appliance Operational Dependencies

Two-way communication facilities and advanced metering infrastructure enable residential buildings to be capable of actively participating in demand side management schemes. This paper proposes a new home energy management system (HEMS), which optimally schedules the operation of home energy resources, with the aim to minimize the home’s one-day electricity cost charged by the real-time pricing while taking into account the monthly basis peak power consumption penalty, charged by the demand charge tariff. To better ensure the user’s lifestyle requirements, the HEMS also models lifestyle-related operational dependencies of household appliances. The numerical simulations and case studies are conducted to validate the reasonability of the proposed method.

Minimizing Energy Consumption and Peak Power of Series Elastic Actuators: A Convex Optimization Framework for Elastic Element Design.

Compared to rigid actuators, series elastic actuators (SEAs) offer a potential reduction of motor energy consumption and peak power, though these benefits are highly dependent on the design of the torque-elongation profile of the elastic element. In the case of linear springs, natural dynamics is a traditional method for this design, but it has two major limitations-arbitrary load trajectories are difficult or impossible to analyze and it does not consider actuator constraints. Parametric optimization is also a popular design method that addresses these limitations, but solutions are only optimal within the space of the parameters. To overcome these limitations, we propose a nonparametric convex optimization program for the design of the nonlinear elastic element that minimizes energy consumption and peak power for an arbitrary periodic reference trajectory. To obtain convexity, we introduce a convex approximation to the expression of peak power; energy consumption is shown to be convex without approximation. The combination of peak power and energy consumption in the cost function leads to a multiobjective convex optimization framework that comprises the main contribution of this paper. As a case study, we recover the elongation-torque profile of a cubic spring, given its natural oscillation as the reference load. We then design nonlinear SEAs for an ankle prosthesis that minimize energy consumption and peak power for different trajectories and extend the range of achievable tasks when subject to actuator constraints.

A reconfigurable 4-GS/s power-efficient floating-point FFT processor design and implementation based on single-sided binary-tree decomposition

This paper presents a high throughput size-configurable floating point (FP) Fast Fourier Transform (FFT) processor, having implemented the 8-parallel multi-path delay feedback (MDF) functions suitable for applications in the real-time radar imaging system. With regard to floating-point FFT design, to acquire a high throughput with restricted area and power consumptions poses as a greater challenge due to some higher degrees of complexity involved in realizing of FP operations than those fixed-point counterparts. To address the related issues, a novel mixed-radix FFT algorithm featuring the single-sided binary-tree decomposition strategy is proposed aiming at effectively containing the complexity of multiplications for any 2k-point FFT. To this aid, the parallel-processing twiddle factor generator and the dual addition-and-rounding fused FP arithmetic units are optimized to meet the high accuracy demand in computation and the low power budget in implementation. The proposed FP FFT processor has been designed in silicon based on SMIC's 28 nm CMOS technology with the active area of 1.39 mm2. The prototype design delivers a throughput of 4 GSample/s at 500 MHz, at a peak power consumption of 84.2 mW. Thus, the proposed design approach achieves a significant improvement in power efficiency approximately by 14 times on average over some other FP FFT processors previously reported.

Control of EV Charging and BESS to Reduce Peak Powers in Domestic Real Estate

The paper discusses the effects of electric vehicle (EV) charging and battery energy storage systems (BESS) on monthly peak powers in domestic apartment buildings. The introduced control method for both EV charging and BESS utilizes real-time measurements and memorized peak power consumptions to determine the available power before exceeding the previous peak power. This kind of control method could lead to cost savings if the power-based distribution tariff of distribution system operators (DSO) includes a price component based on monthly peak power. Some Finnish DSOs have also launched this kind of power-based distribution tariff for small-scale customers. Simulations indicate that EVs can likely be charged without increasing the monthly peak powers of real estate when EV penetration is relatively small. Higher EV penetration would lead to a higher risk of some EVs to not fully charge. There are also indications that BESS can be effective in limiting monthly peak powers when utilizing the peak saving control method.

Energy Flows Management of Multiple Electric Vehicles in Smart Grid

This paper presents energy flows management of multiple electric vehicles charging and its impact on a microgrid. The studied low voltage grid comprises also two photovoltaic generators and non-dispatchable loads. Three concepts using electric vehicles batteries as a distributed storage and bidirectional energy transfer in the Smart Grid are considered: Vehicle to Home, Vehicle to Vehicle and Vehicle to Grid. The potential for implementation of flexible microgrid energy flow management strategy for reduction of peaks in power consumption, absorption or production of reactive power (when required) and improving the power quality in the studied microgrid are estimated. For this purpose a set of usual load profiles of electric vehicle charging modes are studied and presented. Simulation results demonstrate the potential of electric vehicles as a flexible load and energy storage for peak load shaving in microgrids.DOI: 10.5755/j01.eie.25.1.22730

Decoding methods for the flow shop scheduling with peak power consumption constraints

The permutation flow shop scheduling problem (PFSP) is of wide application backgrounds and plays an important role in the manufacturing systems. With the serious energy concerns in manufacturing enterprises, peak power consumption is considered one of the significant issues. For the PFSP with peak power consumption constraints (PFSPP), the real-time power consumption cannot exceed a given peak power at any time. Since the classical first-come first-serve scheduling method is not suitable for the PFSPP, this paper addresses the decoding methods to obtain feasible schedules based on the permutation encoding scheme. First, an earliest processing rule (EPR) is designed to determine the starting time of each operation, satisfying the power consumption constraints. Then, five decoding methods based on EPR are proposed to determine the suitable priority between the operations to yield feasible schedules with high quality. After analysing the complexity of the proposed decoding methods and comparing the performances via extensive numerical tests, some suggestions are provided for solving the PFSPP with different scales and power constraints.