Power Consumption Strategy Research Articles

Optimal economic operation of smart home devices based on home microgrids and time-of-use tariffs

Abstract In this paper, a two-layer model is proposed for smart devices in a home energy management system in order to optimize residential energy use under time-of-use tariffs and home microgrids. Home smart devices are modelled based on their power consumption attributes and building thermal resistance. An HVAC-transferable device cooperative operation strategy is introduced to construct the economic optimization of the upper-level HVAC operation and the lower-level transferable device operation. This strategy reduces home power costs while maintaining user comfort. By iteratively solving the two-layer model, the optimal power consumption scheme for home smart devices is determined.

Research on an invasive and low-frequency power consumption data acquisition method

Aiming at the large-scale, compatibility and reliability problems faced by the data acquisition engineering for power consumption in the industrial field, for reducing the complexity and cost of the data acquisition engineering, and improving the quality, maintainability, scalability and manageability of the data acquisition, an engineering-oriented, intrusive, low-frequency data acquisition scheme for power consumption is proposed. The scheme leverages multi-agent networking technology to solve the large-scale problem, utilizes the communication driver of the dynamic loading adapter mode to tackle the compatibility trouble. Furthermore, the local storage, breakpoint retransmission, flow control, automatic recovery, automatic connection, and time synchronization are combined to solve the reliability issue. Engineering tests show that the proposed scheme possesses the significant backward compatibility characteristics, which can effectively reduce the complexity and cost of data acquisition engineering for power consumption, and significantly improve the quality, maintainability, scalability and manageability of data acquisition.

Energy-Efficient Low-Complexity Algorithm in 5G Massive MIMO Systems

Energy efficiency (EE) is a critical design when taking into account circuit power consumption (CPC) in fifth-generation cellular networks. These problems arise because of the increasing number of antennas in massive multiple-input multiple-output (MIMO) systems, attributable to inter-cell interference for channel state information. Apart from that, a higher number of radio frequency (RF) chains at the base station and active users consume more power due to the processing activities in digital-to-analogue converters and power amplifiers. Therefore, antenna selection, user selection, optimal transmission power, and pilot reuse power are important aspects in improving energy efficiency in massive MIMO systems. This work aims to investigate joint antenna selection, optimal transmit power and joint user selection based on deriving the closed-form of the maximal EE, with complete knowledge of large-scale fading with maximum ratio transmission. It also accounts for channel estimation and eliminating pilot contamination as antennas M → ∞. This formulates the optimization problem of joint optimal antenna selection, transmits power allocation and joint user selection to mitigate inter-cell-interference in downlink multi-cell massive MIMO systems under minimized reuse of pilot sequences based on a novel iterative low-complexity algorithm (LCA) for Newton’s methods and Lagrange multipliers. To analyze the precise power consumption, a novel power consumption scheme is proposed for each individual antenna, based on the transmit power amplifier and CPC. Simulation results demonstrate that the maximal EE was achieved using the iterative LCA based on reasonable maximum transmit power, in the case the noise power is less than the received power pilot. The maximum EE was achieved with the desired maximum transmit power threshold by minimizing pilot reuse, in the case the transmit power allocation ρd = 40 dBm, and the optimal EE = 71.232 Mb/j.

Design of power and resource management in OFDMA networks using sleep mode selection technique

This paper considers the consumption of power and resource metrics in small cell networks, which are enabled through the Sleep Mode Selection (SLS) function in Orthogonal Frequency Division Multiple Access (OFDMA) system. The proposed technique considers heterogeneous networks and operates under two different schemes that includes power management using SLS and resource management using a deactivation algorithm. Further, the limitation of low power consumption schemes leads to degradation of network performance in Orthogonal Frequency Division Multiple Access (OFDMA) networks. Hence, to improve the performance, a Semi-Markov SLS (SMD-SLS) decision making process is utilized in the present study. The performance is again improved with spectrum reuse maximization, where a small cell deactivation strategy identifies well the cells in sleep modes condition and eliminates them optimally for improved spectrum reuse. The performance is evaluated for the conventional non-sleeping mode cells and a benchmark technique. The results validates the performance of proposed model against improved resource and power management.

Three-layer day-ahead scheduling for active distribution network by considering multiple stakeholders

Abstract The increasing penetration of renewable energy resources (RES) has prompted the gradual evolution of distribution systems from passive networks to active ones. The independent operators of various microgirds and active users urgently need a new optimization scheme to replace the traditional centralized scheduling method in active distribution network (ADN). This paper proposes a three-layer day-ahead optimal schedule mechanism taking account of multi-stakeholders in ADN. In the proposed mechanism, the distribution network (DN) is divided into three layers structurally: User layer, microgrid (MG) layer and DN layer. Meanwhile, the corresponding three-layer optimization method is proposed correspondingly. Firstly, the optimal user power consumption scheme and the optimal user energy storage operation scheme can be obtained by minimizing the total cost of an active user. Subsequently, the MG economic dispatch is performed within the obtained user power purchase information from the User layer. By ensuring the interests of active users, an optimal MG economic dispatch scheme can be obtained, in which the information of the connection point between the DN and the MG is transmitted to the upper DN as one of the constraints in the optimization of DN layer. Finally, the third-layer optimization is performed to obtain the optimization results of the DN layer. An actual 47-bus distribution system is employed to verify the proposed three-layer optimizing framework and the results show the effectiveness of the proposed method.

Improving Power and Resource Management in Heterogeneous Downlink OFDMA Networks

In the past decade, low power consumption schemes have undergone degraded communication performance, where they fail to maintain the trade-off between the resource and power consumption. In this paper, management of resource and power consumption on small cell orthogonal frequency-division multiple access (OFDMA) networks is enacted using the sleep mode selection method. The sleep mode selection method uses both power and resource management, where the former is responsible for a heterogeneous network, and the latter is managed using a deactivation algorithm. Further, to improve the communication performance during sleep mode selection, a semi-Markov sleep mode selection decision-making process is developed. Spectrum reuse maximization is achieved using a small cell deactivation strategy that potentially identifies and eliminates the sleep mode cells. The performance of this hybrid technique is evaluated and compared against benchmark techniques. The results demonstrate that the proposed hybrid performance model shows effective power and resource management with reduced computational cost compared with benchmark techniques.

An Efficient and Accurate Nonintrusive Load Monitoring Scheme for Power Consumption

Nonintrusive load monitoring (NILM) has attracted tremendous attention owing to its cost efficiency in electricity and sustainable development. NILM aims at acquiring individual appliance power consumption rates using an aggregated power smart meter reading. Each individual appliance’s power consumption enables users to monitor their electricity usage habits for rational saving strategies. This is also a valuable tool for detecting failure in appliances. However, the major barriers facing NILM schemes are issues of accurately capturing the features of each appliance and decreasing the computing time. Motivated by these challenges, we propose a new, efficient, and accurate NILM scheme, consisting of a learning step and a decomposing step. In the learning step, we propose the fast search-and-find of density peaks (FSFDPs) clustering algorithm aimed at capturing the features of the power consumption patterns of appliances. In the decomposing step, we propose a genetic algorithm (GA)-based matching algorithm to estimate the power consumption of each individual appliance using the aggregated power reading. Using elitist and catastrophic strategies, this step reduces the searching space to achieve considerable efficiency. Experimental results using the reference energy disaggregation dataset (REDD) indicate that our proposed scheme promotes accuracy by 10% and reduces the decomposing time by half.

Joint Balanced Routing and Energy Harvesting Strategy for Maximizing Network Lifetime in WSNs

Nowadays, wireless sensor networks (WSNs) are becoming increasingly popular due to the wide variety of applications. The network can be utilized to collect and transmit numerous types of messages to a data sink in a many-to-one fashion. The WSNs usually contain sensors with low communication ability and limited battery power, and the battery replacement is difficult in WSNs for large amount embedded nodes, which indicates a balanced routing strategy is essential to be developed for an extensive operation lifecycle. To realize the goal, the research challenges require not only to minimize the energy consumption in each node but also to balance the whole WSNs traffic load. In this article, a Shortest Path Tree with Energy Balance Routing strategy (SPT-EBR) based on a forward awareness factor is proposed. In SPT-EBR, Two methods are presented including the power consumption and the energy harvesting schemes to select the forwarding node according to the awareness factors of link weight. First, the packet forwarding rate factor is considered in the power consumption scheme to update the link weight for the sensors with higher power consumption and mitigate the traffic load of hotspot nodes to achieve the energy balance network. With the assistance of the power consumption scheme, hotspot nodes can be transferred from the irregular location to the same intra-layer from the sink. Based on this feature, the energy harvesting scheme combines both the packet forwarding rate and the power charging rate factors together to update the link weight with a new battery charging rate factor for hotspot nodes. Finally, simulation results validate that both power consumption and energy harvesting schemes in SPT-EBR achieve better energy balance performance and save more charging power than the conventional shortest path algorithm and thus improve the overall network lifecycle.

Adaptive power consumption improves the reliability of solar-powered devices for internet of things

Photovoltaics (PV) can provide power autonomy to sensors and communication devices comprising the Internet of Things (IoT). An outstanding challenge is to create design rules that transform intermittent and non-dispatchable solar energy into persistent, stable, and low-cost power for sensors. These design rules govern optimal system architecture design and optimal power consumption patterns. In previous work, we considered (i) the variability of solar insolation across different time scales (hourly, daily, and seasonal); (ii) the solar cell device characteristics; and (iii) the power consumption of the sensing and communication modules. From these constraints, we described a methodology to calculate the necessary energy storage capacity to reliably power the device over several years. The system size can then be optimized to minimize cost or volume. In this paper, we describe a methodology to improve the reliability of solar-powered IoT devices in the event of outlier conditions (i.e., periods of anomalously low insolation that reduce battery state-of-charge). This theoretical study, based on actual sensor and insolation data, describes how an adaptive power consumption scheme decreases the dimensions of solar cell and battery as well as system cost, assuming flexible operating scheme is allowed. The adaptive power consumption scheme means the data acquisition and transmission rate — which governs the power consumption profile — changes based on availability of stored energy in the battery and recent solar insolation. The results show for 3% reduction in average data transmission, the cost of the power components (i.e. battery and solar panel) can be reduced by 10% and the volume of the battery by 30%. The design is constrained by the available power as well as the minimum expected system performance, using the rate of acquired data from the sensor. We find that implementing an adaptive power consumption scheme based on available battery capacity for solar powered sensor can reduce battery capacity by 90%, while ensuring that data transmission rate occasionally varies but never extinguishes completely.

Energy‐efficient precoding design for cloud radio access networks

In cloud radio access network, a baseband unit (BBU) performs the baseband processing for a cluster of low-power low-cost remote radio heads (RRHs) that are connected to the BBU through low-latency fronthaul links. In this study, the authors study the optimisation of two energy-efficient compression and precoding strategies which take transmit power constraint, fronthaul capacity constraint and user specific rate constraint into account. To overcome the non-convexity nature of the original problem, they first transform the objective of the original problem into a parameterised subtractive form and obtain an approximate convex problem via the successive convex approximation. Then, an effective optimisation algorithm with provable convergence is designed to solve the effective problem. Numerical results reveal that the proposed scheme outperforms the conventional maximum sum rate and minimum total power consumption schemes in terms of the energy-efficiency criterion. In particular, compression after precoding strategy outperforms compression before precoding strategy when both of their RRHs perform the same user scheduling, while the opposite conclusion can be drawn otherwise.

Environmental benefits analysis based on life cycle assessment of rice straw-based synthetic natural gas in China

A cradle-to-gate life cycle assessment of rice straw-based synthetic natural gas (R-SNG) production was carried out in this study, and the inventories of rice straw cultivating and harvesting, feedstock transportation, R-SNG facility construction and dismantling, and power consumption (i.e. purchased electricity (PE) scheme and self-supply power (SP) scheme) stages were analyzed. Then, the inventory data was characterized and normalized. The results showed that the SP scheme can significantly reduce the environmental impacts of R-SNG product. A comparison of R-SNG, natural gas (NG) and coal-based synthetic natural gas (C-SNG) was conducted from the perspective of environmental impacts. The results showed that the amount of CO2 emission from C-SNG was the largest, followed by that from NG, while the amount of CO2 emission from R-SNG was negative. A sensitivity analysis about allocation principles of the rice straw and rough rice was carried out to evaluate the allocation principles on the environmental impacts. In conclusion, R-SNG showed great environmental benefits in the abiotic resources saving and greenhouse gases emission reduction.

Research on intelligent power consumption strategy based on time-of-use pricing

In this paper, through the analysis of shortcomings of the current domestic and foreign household power consumption strategy: Passive way of power consumption, ignoring the different priority of electric equipment, neglecting the actual load pressure of the grid, ignoring the interaction with the user, to decrease the peak-valley difference and improve load curve in residential area by demand response (DR technology), an intelligent power consumption scheme based on time-of-use(TOU) pricing for household appliances is proposed. The main contribution of this paper is: (1) Three types of household appliance loads are abstracted from different operating laws of various household appliances, and the control models and DR strategies corresponding to these types are established. (2) The fuzzified processing for the information of TOU price, which is based on the time intervals, is performed to get the price priority, in accordance with such DR events as the maximum restricted load of DR, the time of DR and the duration of interruptible load and so on, the DR control rule and pre-scheduling mechanism are led in. (3) The dispatching sequence of household appliances in the control and scheduling queue are switched and controlled to implement the equilibrium of peak and valley loads. The equilibrium effects and economic benefits of power system by pre-scheduling and DR dispatching are compared and analyzed by simulation example, and the results show that using the proposed household appliance control (HAC) scheme the overall cost of consumers can be reduced and the power system load can be alleviated, so the proposed household appliance control (HAC) scheme is feasible and reasonable.

A crossbar resistance switching memory readout scheme with sneak current cancellation based on a two-port current-mode sensing

This paper describes a novel readout scheme that enables the complete cancellation of sneak currents in resistive switching random-access memory (RRAM) crossbar array. The current-mode readout is employed in the proposed readout, and a few critical advantages of the current-mode readout for crossbar RRAM are elucidated in this paper. The proposed scheme is based on a floating readout scheme for low power consumption, and one more sensing port is introduced using an additional reference word line. From the additional port, information on the sneak current amount is collected, and simple current-mode arithmetic operations are implemented to cancel out the sneak current from the sensing current. In addition, a simple method of handling the overestimated-sneak-current issue is described. The proposed scheme is verified using HSPICE simulation. Moreover, an example of a current-mode sense amplifier realizing the proposed cancelling technique is presented. The proposed sense amplifier can be implemented with less hardware overhead compared to the previous works.

홈 게이트웨이에서 서비스 특성에 따른 버퍼 동작 시간 제어를 통한 전력 소비 감소 방안

본 논문은 홈 게이트웨이에서 타이머를 고려한 슬립 모드를 이용하여 효과적인 전력 소비 방안을 제안한다. 본 논문에 의하면 홈 게이트웨이로 유입되는 패킷을 실시간 패킷과 비실시간 패킷으로 구분하고 비실시간 패킷을 지연시킨다. 따라서 비실시간 패킷은 타이머를 고려한 메커니즘을 통해 추가적인 대기시간을 얻음으로써 홈 게이트웨이의 슬립 시간을 증가 시킬 수 있다. 성능 분석을 위하여 non-preemptive two priority queueing 모델을 이용하였다. 그 결과, 비실시간 트래픽을 지연시킴으로써 기존 방안에 비하여 제안 방안의 소비되는 전력이 감소됨을 확인할 수 있다. This paper proposes an efficient power consumption scheme using sleep mode in home gateway. The scheme by this paper classifies incoming real time packet and non-real time packet in home gateway and delay non-real time packet. Therefore, the home gateway can have longer sleep time because non-real time packet can get additional delay time by proposing mechanism using timer. We use non-preemptive two priority queueing model for performance analysis. As a results, we verify that power consumption of proposed scheme is reduced more than existing scheme by delay of non-real time traffic.

Wave-pipelined intra-chip signaling for on-FPGA communications

On-FPGA communication is becoming more problematic as the long interconnection performance is deteriorating in technology scaling. In this paper, we address this issue by proposing a novel wave-pipelined signaling scheme to achieve substantial throughput improvement in FPGAs. A new analytical model capturing the electrical characteristics in FPGA interconnects is presented. Based on the model, throughput and power consumption of a wave-pipelined link have been derived analytically and compared to the conventional synchronous links. Two circuit designs are proposed to realize wave-pipelined link using FPGA fabrics. The proposed approaches are also compared with conventional synchronous and asynchronous pipelining techniques. It is shown that the wave-pipelined approach can achieve up to 5.7 times improvement in throughput and 13% improvement in power consumption versus conventional delay-based on-chip communication schemes. Also, trade-offs between power, throughput and area consumption between the proposed and conventional designs are studied. The wave-pipelining approach provides a new alternative for on-FPGA communications and can potentially become a promising solution to mitigate the future interconnect scaling challenge.

Computationally efficient discriminators for code-tracking loops

New and revisited algorithms for coherent and noncoherent code-tracking loops (CTL) are presented in this work. The algorithms are relatively simple in terms of computational effort and hardware reduction. Functional identity to traditionally used algorithms is shown. The increased computational efficiency of the proposed schemes is provided by reducing substantially the multiply and accumulate (MAC) operations per time unit. Reducing the computational effort results in reduced power consumption schemes both for hardware and software implemented loop discriminators.