Aggregate Power Research Articles

Uncertainty quantification for appliance recognition in non-intrusive load monitoring using Bayesian deep learning

Non-Intrusive Load Monitoring (NILM) can be used to detect, recognize, and classify switching events of individual electrical appliances from an aggregate power signal that is measured at the main line of the grid connection. A limitation of existing solutions is that deep learning models tend to overfit the data and do not express their uncertainty when making predictions. This paper shows that uncertainty information can be obtained in a natural way by making use of Bayesian Neural Networks. Having this information is very valuable, because it supplies relevant information about potential misclassifications of the model to an end-user. The source of these misclassifications can be attributed to ambiguous data, or the model requiring more examples to learn from. In this work, an increase in generalization performance is observed when making use of Stochastic Gradient Hamiltonian Monte Carlo over Stochastic Gradient descent, and the usefulness of uncertainty in a NILM context is discussed.

Clustering and Markov Model of Plug-In Electric Vehicle Charging Profiles for Peak Shaving Applications

The charging profiles of plug-in electric vehicles (PEVs) have large volatility. It has brought great challenges for aggregator to accurately complete load identification and aggregated configuration. Therefore, an analysis and configuration method of responsive capacity based on clustering and the Markov model are proposed in this paper. Firstly, the adaptive two-scale clustering algorithm based on spectral clustering (ATCSC) is applied to the clustering of charging piles. The offset compensation of the extreme points is used to form the distance measurement in the clustering process. Then, the responsive aggregated power can be obtained after the change control of suitable charging piles. Finally, the variation characteristics of the actual charging profiles based on the Markov model are introduced to the reliability evaluation in the load curtailment service. Simulation results reveal the following. (1) The robustness of the proposed method is better especially for the PEV charging profiles with strong volatility. (2) The validity of the aggregated configuration is verified. Additionally, the sum of power deviation is 0.0707 kW when the change interval of control strategy is 15 min. (3) The maximum shortage of configuration is −98.0875 kW as the entropy of the volatility is 37.027.

Stochastic geometry analysis of coverage probability in energy efficient dense heterogeneous network with sleep control mechanism

The upsurge of data traffic in the macrocell networks has led to the massive deployment of small cells (SCs) for load sharing. Though small cell power consumption is low individually, significant aggregate power consumption, together with the MBS transmission power that is exponentially increasing over its served user load, deteriorates the network energy efficiency (EE). Consequently, EE is seen as a critical requirement for wireless network design in future. The challenge of EE maximization in dense heterogeneous networks is investigated in this research through a strategic small cell sleeping technique. A heuristic method based on distance and load awareness strategy in which the small cells with fewer users near the macrocell will be put into sleep mode. User equipments (UEs) under the service of the sleep small cells will be offloaded to the MBS. A dense heterogeneous network is considered with overlapped small cell coverage regions. All the edge area SCs will be kept ON to avoid QoS degradation. Simulation analysis on the stochastic geometry model indicates that the proposed sleep strategy significantly improves the network EE than the prevailing sleep control strategies while assuring seamless, efficient coverage in the sleep cell areas.

Strategi Amerika Serikat dalam Membendung Kebangkitan Tiongkok di Asia Pasifik melalui Aliansi Pertahanan Quadrilateral Periode 2017-2020

This paper aims to find out the strategic role of the US Quadrilateral Security Alliance in reducing China's threats in the Asia Pacific during 2017-2020. The method used in this research is qualitative, with descriptive-analytical technique. This technique aims to provide a holistic understanding and description of a symptom or phenomenon. The findings of this study show that the rise of China in the economic and military sectors, as well as China's aggressiveness in expanding its territories, threatens the US hegemony in the Asia Pacific. Thus, to maintain its status quo, the US formed a Quad Alliance with Australia, Japan and India in 2007. However, this alliance was inactive since 2008 and reactivated in 2017 by President Donald Trump. The Quad Alliance is quite intense in conducting military expeditions in the Indian-Pacific Ocean. Based on the Balance of Threat theory by Stephen Walt (1985), the Quad Alliance is the US "Rebalancing Strategy" against China in the Asia Pacific, which aims to eliminate 4 main sources of threats, namely China's aggregate power, China's geographical proximity to the US alliance, China's military capabilities, and China's ambitions in the region. The dynamics of the Sino-US contestation are driven by the basic motive of Neorealism, that is protecting the national interests of the two countries, whether political, economic, or domination/influence.

Reliability evaluation of an aggregate power conversion unit in the off-grid PV-battery-based DC microgrid from local energy communities under dynamic and transient operation

In a rural/local energy community (LEC), a PV-battery-based DC microgrid (MG) normally operates at the off-grid/islanded mode. At this off-grid operation mode, the reliability of power converters in both the PV system and the battery energy storage system (BESS) could be reduced by dynamic operation scenarios of the DC microgrid which are frequently repeated, such as the intermittency of PV-system output power, the random fluctuation of load power. Indeed, the dynamic operation of the PV-generating system and the load system in the off-grid DC microgrid could lead to a certain decrease in the reliability of DC–DC bidirectional converters of the BESS, boost converters of the PV system, and buck converters for low-voltage DC loads because of withstanding different power flows to provide a source-load power balance in the DC microgrid. Moreover, transient operation scenarios of the off-grid DC microgrid can significantly impact the reliability of the mentioned power converters. To make the above assumptions more clear, reliability analysis of an aggregate power conversion unit (APCU) in the off-grid PV-battery-based DC microgrid under dynamic and transient operation in the local energy community is presented in this paper. The APCU contains the boost converter of the PV system, the bidirectional converter of the BESS, and the buck converter of the low-voltage DC load system. The main objective of the paper is to shed light on calculations of the dynamic-voltage-dependent failure rate (DVDFR) and fault-current-dependent failure rate (FCDFR) of the APCU from dynamic and transient operation conditions respectively in the off-grid LVDC microgrid. Then, a novel combination of the useful-time-dependent failure rate (UTDFR), the DVDFR, and the FCDFR is proposed to evaluate the system-level and component-level reliability of the APCU in the off-grid DC microgrid. Markov-state transition diagrams are applied for the APCU’s reliability assessment. Experimental results show that the reliability of the bidirectional power converter is more affected by the dynamic and transient operation than that of the boost or buck converters in the APCU. In addition, the DVDFR of the APCU is almost smaller than its FCDFR, but the system-level reliability of the APCU could be significantly reduced by dynamic cases in the islanded DC microgrid. Last but not least, the MTTF and MTBF of the APCU could be dramatically decreased by the dynamic and transient operation of the DC microgrid.

On optimal energy-extraction performance of arrays of wave-energy converters, with full consideration of wave and multi-body interactions

An array of ocean-wave energy converters, compared to a stand-alone device, provides lower energy-production costs but affects the aggregate power performance. Previous studies suggested that wave-interaction effects within the array could increase energy production by over 15% per device. However, no physical constraints on the WECs were considered in these studies; as a result, the devices can experience unrealistically large motions. A control strategy is thus desirable to coordinate individual WECs and maximize the energy extraction. We apply model-predictive control (MPC) to an array of point-absorber-type WECs by developing a formulation that accommodates hard constraints on the motion of each device and on the Power-takeoff (PTO) forces with excellent computational efficiency. Full multi-body wave-interaction effects are computed by a semi-analytical method within linear-wave theory. This framework enables us to demonstrate results of realistic maximal power production for arrays of two, three, and four devices operating under constraints in sea waves of various incident angles. It is observed that wave–body interactions have actually destructive effects on the array power production for the majority cases in irregular sea waves. Also discussed are effects of the reactive power produced by the PTO unit and the possibility of removing it within the MPC framework.

Numerosity tuning in human association cortices and local image contrast representations in early visual cortex

Human early visual cortex response amplitudes monotonically increase with numerosity (object number), regardless of object size and spacing. However, numerosity is typically considered a high-level visual or cognitive feature, while early visual responses follow image contrast in the spatial frequency domain. We find that, at fixed contrast, aggregate Fourier power (at all orientations and spatial frequencies) follows numerosity closely but nonlinearly with little effect of object size, spacing or shape. This would allow straightforward numerosity estimation from spatial frequency domain image representations. Using 7T fMRI, we show monotonic responses originate in primary visual cortex (V1) at the stimulus’s retinotopic location. Responses here and in neural network models follow aggregate Fourier power more closely than numerosity. Truly numerosity tuned responses emerge after lateral occipital cortex and are independent of retinotopic location. We propose numerosity’s straightforward perception and neural responses may result from the pervasive spatial frequency analyses of early visual processing.

Coordinated dispatch strategy of air conditioners for providing load regulation service

As the penetration of renewables increases, air conditioners (ACs), whose electricity consumptions account for a large share, present tremendous potential in providing load regulating service to maintain the power supply–demand balance. In this paper, an analytical model for the dynamic aggregated power and schedulable capacity of ACs is presented, where the safe protocol (SP) is utilized to eliminate the power oscillations caused by temperature setpoint shifting. Based on this, the response potential of ACs is quantified by the evaluation indexes in the dimensions of capacity and time. Besides, an optimal dispatch strategy is proposed to implement the coordination of air conditioning resources in the response stage and recovery stage, by which not only the reliable load regulation service can be provided, but also the influences on users can be minimized. Simulation studies demonstrate the effectiveness of the proposed method.

Multi-label Deep Convolutional Transform Learning for Non-intrusive Load Monitoring

The objective of this letter is to propose a novel computational method to learn the state of an appliance (ON / OFF) given the aggregate power consumption recorded by the smart-meter. We formulate a multi-label classification problem where the classes correspond to the appliances. The proposed approach is based on our recently introduced framework of convolutional transform learning. We propose a deep supervised version of it relying on an original multi-label cost. Comparisons with state-of-the-art techniques show that our proposed method improves over the benchmarks on popular non-intrusive load monitoring datasets.

Power Muirhead mean in spherical normal fuzzy environment and its applications to multi-attribute decision-making

This study aims to propose the power Muirhead mean (PMM) operator in the spherical normal fuzzy sets (SNoFS) environment to solve multiple attribute decision-making problems. Spherical normal fuzzy sets better characterize real-world problems. On the other hand, the Muirhead mean (MM) considers the relationship between any number of criteria of the operator. Power aggregation (PA) reduces the negative impact of excessively high or excessively low values on aggregation results. This article proposes two new aggregation methods: spherical normal fuzzy power Muirhead mean (SNoFPMM) and spherical normal fuzzy weighted power Muirhead mean (SNoFWPMM). Also, these operators produce effective results in terms of their suitability to real-world problems and the relationship between their criteria. The proposed operators are applied to solve the problems in choosing the ideal mask for the COVID-19 outbreak and investment company selection. However, uncertainty about the effects of COVID-19 complicates the decision-making process. Spherical normal fuzzy sets can handle both real-world problems and situations involving uncertainty. Our approach has been compared with other methods in the literature. The superior aspects and applicability of our strategy are also mentioned.

Non-Intrusive Load Monitoring of Household Devices Using a Hybrid Deep Learning Model through Convex Hull-Based Data Selection

The availability of smart meters and IoT technology has opened new opportunities, ranging from monitoring electrical energy to extracting various types of information related to household occupancy, and with the frequency of usage of different appliances. Non-intrusive load monitoring (NILM) allows users to disaggregate the usage of each device in the house using the total aggregated power signals collected from a smart meter that is typically installed in the household. It enables the monitoring of domestic appliance use without the need to install individual sensors for each device, thus minimizing electrical system complexities and associated costs. This paper proposes an NILM framework based on low frequency power data using a convex hull data selection approach and hybrid deep learning architecture. It employs a sliding window of aggregated active and reactive powers sampled at 1 Hz. A randomized approximation convex hull data selection approach performs the selection of the most informative vertices of the real convex hull. The hybrid deep learning architecture is composed of two models: a classification model based on a convolutional neural network trained with a regression model based on a bidirectional long-term memory neural network. The results obtained on the test dataset demonstrate the effectiveness of the proposed approach, achieving F1 values ranging from 0.95 to 0.99 for the four devices considered and estimation accuracy values between 0.88 and 0.98. These results compare favorably with the performance of existing approaches.

Storage Minimization of Marine Energy Grids Using Polyphase Power

Multiple wave energy converter (WEC) buoys can be used to establish a WEC array-powered microgrid collectively forming a Marine Energy Grid (MEG). An oceanic domain with gravity waves will have significant spatial variability in phase, causing the power produced by a WEC array to have high peak-to-average ratios. Minimizing these power fluctuations reduces the demand for large energy storage by WEC array-powered DC microgrids while also reducing losses in the undersea cable to the shore. Designs that reduce energy storage requirements are desirable to reduce deployment and maintenance costs. This work demonstrates that polyphase power in conjunction with an energy storage system can be used to maintain constant power. This work shows that an N WEC array geometry can be designed to reduce the energy storage requirements needed to mitigate the power fluctuations if the WEC array produces constant, polyphase power. Additionally, the conditions that identify the wave frequencies and control the effort needed to produce polyphase power are developed. This paper also shows that increasing the number of WECs in an array reduces aggregate power fluctuations. Finally, WEC array power profiles are investigated using simulation results to verify the mathematical conditions developed for the three and six WEC cases.

Minimum required power capacity of tractors depending on grain cultivation methods

The paper identifies aggregate power capacity produced by tractor engines on a farm engaged in spring grain cultivation alternating methods for primary and secondary tillage by years and crop rotations. The required power capacity is calculated based on an energy tractor-implement model in primary, secondary tillage and sowing operations. Power capacity is enhanced with due account of measurements and quantity of equipment to have an impact on spring grain harvest. The minimum power capacity is required for no till methods (0.93-1.42 kW/ha). The maximum power capacity is required for deep autumn plowing, spring harrowing and, after 5-8 days, simultaneous cultivation, sowing and rolling with single-operation implements (3.3 kW/ha). The optimal weight of a universal tractor for performing all technological operations is 13 tons, its engine power is 366 kW. The number of tractors required to cultivate 1000 hectares of arable land for spring crops is 9. With these measurements, the total energy consumption for the above methods, being alternated by years, will be minimal.

Conditional Equity Premium and Aggregate Corporate Investment

AbstractWe document a strong negative relation between aggregate corporate investment and conditional equity premium estimated from direct stock market risk measures. Consistent with the investment‐based asset pricing model, the comovement with conditional equity premium fully accounts for aggregate investment's market return predictive power. Similarly, conditional equity premium is a significant determinant of classic Tobin's q measure, although q has much weaker explanatory power for aggregate investment possibly because of its measurement errors. Moreover, the positive relation between aggregate investment and investor sentiment documented in previous studies reflects the fact that both variables correlate closely with conditional equity premium.

Applications of Cubic Schweizer–Sklar Power Heronian Mean to Multiple Attribute Decision‐Making

The cubic set (CS) is a basic simplification of several fuzzy notions, including fuzzy set (FS), interval‐valued FS (IVFS), and intuitionistic FS (IFS). By the degrees of IVFS and FS, CS exposes fuzzy judgement, and this is a much more potent mathematical approach for dealing with information that is unclear, ambiguous, or indistinguishable. The article provides many innovative operational laws for cubic numbers (CNs) drawn on the Schweizer–Sklar (SS) t‐norm (SSTN), and the SS t‐conorm (SSTCN), as well as several desired properties of these operational laws. We also plan to emphasise on the cubic Schweizer–Sklar power Heronian mean (CSSPHM) operator, as well as the cubic Schweizer–Sklar power geometric Heronian mean (CSSPGHM) operator, in order to maintain the supremacy of the power aggregation (PA) operators that seize the complications of the unsuitable information and Heronian mean (HM) operators that contemplate the interrelationship between the input data being aggregated. A novel multiple attribute decision‐making (MADM) model is anticipated for these freshly launched aggregation operators (AOs). Finally, a numerical example of enterprise resource planning is used to validate the approach’s relevance and usefulness. There is also a comparison with existing decision‐making models.

Real-Time Energy Disaggregation Algorithm Based on Multi-Channels DCNN and Autoregressive Model

Energy disaggregation refers to the process of obtaining the energy consumption of several appliances in a house by disaggregating the aggregate power consumption measured by an electrical meter. Currently, deep learning methods are widely applied in this field. Real-time energy disaggregation is an important branch of energy disaggregation. Based on the Short Sequence-to-Point (Short Seq2point) (Odysseas) network structure, a real-time energy disaggregation algorithm based on multi-channels deep convolutional neural networks(MC-DCNN) and autoregressive model(AR) is proposed in this paper, which obtains the energy consumption of appliances at the current time point by disaggregating the historical aggregate power consumption to achieve delivering disaggregation results in real-time. The proposed method takes the original aggregate power sequence and differential power signal as the input of the network, and extracts the information of different time lengths in the sequence using multi-channels deep convolutional neural networks with a modified concatenate layer, so that the network can adapt to different appliances with different operating modes. In addition, the traditional autoregressive model is added as the linear component for solving the problem that the scale of the output is insensitive to the scale of the input in the neural network model. Finally, the proposed method was tested on the UK-DALE and REDD datasets, and the experimental results show that the method has good disaggregation performance on both datasets, has a small number of parameters and achieves fast inference.

Power System Frequency Control Architecture Combining Open Charge Point Protocol and Electric Vehicle Model Predictive Charge Rate Control

This research proposes a power system frequency control architecture which leverages a rising open protocol for charge rate control of electric vehicles: Open Charge Point Protocol. Unlike conventional research that focused on building a high-performance controller, this research puts emphasis on the architecture. Specifically, this research explores the design of the frequency control architecture around a basic functionality of an open protocol while allowing substantial performance to mitigate the need for a well-tuned specialized protocol. As the usage of open protocols cannot provide a quick response, the proposed architecture alleviates this limitation by (a) utilizing a hierarchical and asynchronous structure to emulate a faster control interval and (b) providing a model predictive controller with system integrity protection scheme behavior to have sufficient performance under both large and small disturbances. The overall architecture is tested against an aggregated power system frequency response model on Simulink for both large and small disturbances. Compared to a tuned proportional integral derivative controller on the same architecture, the proposed architecture observed an average reduction of 21.77% in nadir in the step disturbance test and an average reduction of 36.27% in standard deviation from the nominal frequency in the load variation test.

Aggregate Temporally Coupled Power Flexibility of DERs Considering Distribution System Security Constraints

The proliferation of distributed energy resources (DERs) makes it possible for the transmission system operator to exploit the DERs' power flexibility. Modeling the aggregate power flexibility of different types of DERs in the distribution system continues to be a vexing scientific problem. This paper rigorously derives the exact mathematical expression of aggregate temporally coupled power flexibility of DERs considering linear distribution system security constraints. A flexibility optimization method and a backtracking elimination method are proposed to calculate the parameters in the aggregate flexibility model. Based on the exact model, an approximate model is proposed to reduce the complexity, which can be used for practical applications. We further developed a transmission system economic dispatch model considering the power flexibility of distribution systems. Finally, numerical tests are carried out on IEEE test systems to verify the correctness of the derived model and the effectiveness of the proposed methods.

Harmonic Radar With Adaptively Phase-Coherent Auxiliary Transmitters

In harmonic radar (HR), the radio frequency transmitter illuminates a nonlinear target (the tag), causing the return signal to consist of harmonics at multiples of the transmitted carrier frequency. Of them, the second harmonic is usually the strongest and the one to which the receiver is tuned. This frequency difference distinguishes the tag reflection from environmental clutter, which remains at the uplink (transmitter to tag) frequency. However, the passive nature of HR tags severely limits the reflected power, and therefore the range of the downlink (tag to receiver) path. We propose to increase the range and/or signal to noise ratio (SNR) by novel restructuring at the physical and signal levels. For this, we accompany the original transmitter with auxiliary transmitters able to send simple tones that are synchronized to arrive at the tag in phase, and we design the receiver to detect an intermodulation component. The resulting range and SNR are much greater than those of the original, conventional HR system, and greater even than if the original system were to transmit with power equal to the aggregate power of our new system. Achieving mutually coherent, i.e., in phase, arrival of the tones at the tag is the focus of the present paper. We provide a system framework that models the tag and the uplink and downlink, then present the adaptive phase coherence algorithm and analyze the probabilistic growth of the output signal power. We also account for the effects of frequency shifts due to transmitter mobility and the frequency offset errors in the transmitter local oscillators.

Overview of Non-Intrusive Load Monitoring: Probabilistic and Artificial Intelligence approaches

Reduction and conservation of electrical energy consumption in residential buildings is the main objective of Non-Intrusive Load Monitoring (NILM) techniques. NILM detects events and estimate the power consumption of individual appliances by analyzing the aggregate power consumption measured at the service entry. Indeed, our major contribution in this paper is to update research works on NILM methodologies by adding the most recent NILM methods proposed in the literature. In this paper we present an overview of NILM and energy disaggregation methods. Then, we discuss the challenges of this technique to provide useful recommendations for future research.