Aggregate Power Research Articles

A unified framework for coordination of thermostatically controlled loads

A collection of thermostatically controlled loads (TCLs) – such as air conditioners – can vary their power consumption within limits to help the balancing authority of a power grid maintain demand supply balance. Doing so requires loads to coordinate their on/off decisions so that the aggregate power consumption tracks a grid-supplied reference. At the same time, each consumer’s quality of service (QoS) must be maintained. While there is a large body of work on TCL coordination, they do not provide guarantees on the reference tracking performance or QoS maintenance, and they do not provide a means to compute a suitable reference signal for power demand of a collection of TCLs. In this work we provide a framework that addresses these two weaknesses. The framework enables coordination of an arbitrary number of TCLs that: (i) is computationally efficient, (ii) is implementable at the TCLs with local feedback and low communication, and (iii) enables reference tracking by the collection while ensuring that temperature and cycling constraints are satisfied at every TCL at all times. The framework is based on a Markov model obtained by discretizing a pair of Fokker–Planck equations derived in earlier work by Malhame and Chong (1985). We then use this model to design randomized policies for TCLs. The balancing authority broadcasts the same policy to all TCLs, and each TCL implements this policy which requires only local measurement to make on/off decisions. Simulation results are provided to support these claims. Matlab implementation is made publicly available.

Power Aggregation Operators of Interval-Valued Atanassov-Intuitionistic Fuzzy Sets Based on Aczel–Alsina t-Norm and t-Conorm and Their Applications in Decision Making

Aczel–Alsina t-norm and t-conorm are important t-norm and t-conorm, and they are extended from algebraic t-norm and t-conorm. Obviously, Aczel–Alsina t-norm and t-conorm are more general than some existing t-norm and t-conorm. Furthermore, the power aggregation (PA) operator is also a very famous and valuable operator which can consider the power relation between any two input parameters. In addition, Interval-valued Atanassov-intuitionistic fuzzy set (IVA-IFS) can easily express uncertain information. In order to fully use their advantages, in this analysis, we extend the PA operators based on Aczel–Alsina t-norm and t-conorm to IVA-IFS and propose the interval-valued Atanassov-intuitionistic fuzzy Aczel–Alsina power averaging (IVA-IFAAPA), interval-valued Atanassov-intuitionistic fuzzy Aczel–Alsina power ordered averaging (IVA-IFAAPOA), interval-valued Atanassov-intuitionistic fuzzy Aczel–Alsina power geometric (IVA-IFAAPG) and interval-valued Atanassov-intuitionistic fuzzy Aczel–Alsina power ordered geometric (IVA-IFAAPOG) operators. Moreover, we discuss the properties of the presented operators such as idempotency, monotonicity, and boundedness. In addition, a multi-attribute decision-making (MADM) procedure is proposed to process the IVA-IF information. Finally, a practical example is used to show the effectiveness and superiority of the proposed method by comparing it with some existing operators.

Nonintrusive Load Monitoring Based on Self-Supervised Learning

Deep learning models for non-intrusive load monitoring (NILM) tend to require a large amount of labeled data for training. However, it is difficult to generalize the trained models to unseen sites due to different load characteristics and operating patterns of appliances between data sets. For addressing such problems, self-supervised learning (SSL) is proposed in this paper, where labeled appliance-level data from the target data set or house is not required. Initially, only the aggregate power readings from target data set are required to pre-train a general network via a self-supervised pretext task to map aggregate power sequences to derived representatives. Then, supervised downstream tasks are carried out for each appliance category to fine-tune the pre-trained network, where the features learned in the pretext task are transferred. Utilizing labeled source data sets enables the downstream tasks to learn how each load is disaggregated, by mapping the aggregate to labels. Finally, the fine-tuned network is applied to load disaggregation for the target sites. For validation, multiple experimental cases are designed based on three publicly accessible REDD, UK-DALE, and REFIT data sets. Besides, state-of-the-art neural networks are employed to perform NILM task in the experiments. Based on the NILM results in various cases, SSL generally outperforms zero-shot learning in improving load disaggregation performance without any sub-metering data from the target data sets.

Distributed Coordination of Charging Stations Considering Aggregate EV Power Flexibility

In recent years, electric vehicle (EV) charging stations have witnessed rapid growth. However, effective management of charging stations is challenging due to individual EV owners' privacy concerns, competing interests of different stations, and the coupling distribution network constraints. To cope with this challenge, this paper proposes a two-stage scheme. In the first stage, the aggregate EV power flexibility region is derived by solving an optimization problem. We prove that any trajectory within the obtained region corresponds to at least one feasible EV dispatch strategy. By submitting this flexibility region instead of the detailed EV data to the charging station operator, EV owners' privacy can be preserved and the computational burden can be reduced. In the second stage, a distributed coordination mechanism with a clear physical interpretation is developed considering the AC power flow based network constraints. We prove that the proposed mechanism converges to the centralized optimum. Case studies validate the theoretical results. Comprehensive performance comparisons are carried out to demonstrate the advantages of the proposed scheme.

UNITED ARAB EMIRATES - ISRAEL RAPPROCHEMENT: A RATIONAL CHOICE OF EMIRATIS

When the UAE and Israel decided to normalize diplomatic relations, later known as the Abraham Accords, the decision sparked global controversy, especially from Islamic countries. One of the countries which vehemently condemned this deal was Iran. Iran viewed the agreement as a form of betrayal of the Palestinian struggle. However, based on an analysis using the Balance of Threat theory, this article concluded that Iran had in fact caused UAE’s decision to normalize relations. It was because Iran, through its superiority of aggregate power, geographical proximity, and offensive policy character, presented a serious threat to the security of the UAE. Thus, the normalization of relations with Israel was the best and rational choice for the UAE in dealing with the Iranian threat. Through this normalization, the UAE has gained a new partner to jointly counterbalance Iran’s power and help in efforts to strengthen its own defense forces.

Nonintrusive Motor Current Signature Analysis

Motor current signature analysis (MCSA) techniques measure slip-related current signals in the supply lines of a squirrel-cage induction machine (SCIM) for slip estimation, fault detection, and motor diagnostics. In a traditional setup for MCSA, current sensors directly measure the machine currents and are usually installed physically close to the machine of interest, e.g., current transducers clamped about the machine stator leads. For grid-connected SCIMs, the slip-related current signals propagate through the power system. Thus, a single aggregate power monitor can potentially perform MCSA for a collection of SCIMs powered by a common electrical service, referred to in this article as nonintrusive MCSA. Current division and system impedances (source impedance, load impedances, and machine parameters) determine the applicability of nonintrusive MCSA. This article demonstrates measurement hardware, modeling, and experimental results for nonintrusive MCSA. Analysis and results investigate the current-source model of a grid-connected SCIM and its interaction with the system impedances. Applied to a distribution system with multiple machines, the techniques inform when nonintrusive MCSA is possible for collections of SCIMs and equivalent to traditional MCSA.

Multilabel Appliance Classification With Weakly Labeled Data for Non-Intrusive Load Monitoring

Non-Intrusive Load Monitoring consists in estimating the power consumption or the states of the appliances using electrical parameters acquired from a single metering point. State-of-the-art approaches are based on deep neural networks, and for training, they require a significant amount of data annotated at the sample level, defined as strong labels. This paper presents an appliance classification method based on a Convolutional Recurrent Neural Network trained with weak supervision. Learning is formulated as a Multiple-Instance Learning problem, and the network is trained on labels provided for an entire segment of the aggregate power, defined as weak labels. Weak labels are coarser annotations that are intrinsically less costly to obtain compared to strong labels. An extensive experimental evaluation has been conducted on the UK-DALE and REFIT datasets comparing the proposed approach to three benchmark methods. The results obtained for different amounts of strongly and weakly labeled data and mixing UK-DALE and REFIT confirm the effectiveness of weak labels compared to fully supervised and semi-supervised benchmarks methods.

Computational Analysis of a Horror Film Trailer Soundtrack

Computational film analysis combines statistics, data science, information visualization, and computer science with the analytical approaches and domain knowledge of the study of film to frame and answer questions about form and style in the cinema. In this article I illustrate the visualisation and analysis of the soundtrack of the trailer for the Korean horror film Into the Mirror (2003). Using the spectrogram and normalized aggregated power envelope to show how the sound design of the trailer evolves over time and functions at different scales. I address a range of practical considerations relevant to the methods demonstrated here relating to the use of mono versus stereo audio files, the use of different sampling rates, and audio normalization. A supporting website with the code for the statistical programming language R used in this analysis is available at https://rpubs.com/nr62_rp33/CFA-into-the-mirror.

Создание стабильной финансовой системы с целью укрепления совокупной мощи государства (на примере Китая)

The authors have attempted to promote the conceptual views of Wang Yongli, a Chinese academic economist with extensive and authoritative practical experience, to the Russian audience of experts in the international monetary and financial sphere. The theoretical summary of the successful practice of China’s financial industry and the justification of the importance of urgent reform of the international monetary and financial system, presented in Wang Yongli’s book “China’s Choice in the Global Financial Turbulence” became the basis for this article. An informative translation of the book’s fragments dedicated to the reforms of the international monetary system highlights two areas, i.e. social positioning of monetary finance and reform of the international monetary system. The article analyzes the main issues identified as a result of China’s rapid financial development and determines possible ways to solve them. The purpose of financial development is to serve the real economy, and promote economic development for creating greater economic and social value through the examination of the relationship between the real economy and the virtual economy, the relationship between currency finance and real economy. The authors introduce a model of interaction of aggregate power and international influence of the country: the international influence of the country’s currency finances and its status in the international monetary and financial system. An appeal to various concepts explaining the global expansion of the US dollar leads to the conclusion that the International Monetary Fund (IMF) needs a deep reconstruction. A detailed analysis of the history of the formation, management and reform of the IMF is given, revealing its inconsistency with global development trends. The article shows the role of China after the 2008 financial crisis and the significance of the yuan in the currency basket. The study represents the author’s position on the transformation of special drawing rights (SDRs) into a super-sovereign world currency. By highlighting the issues Wang Yongli has foreseen many situations and events that are now taking place in the global monetary and financial system.

Intuitionistic fuzzy power Aczel-Alsina model for prioritization of sustainable transportation sharing practices

Traffic congestion and environmental pollution generated by transportation activities significantly endanger the sustainable development of cities. This study presents a new strategy for implementing the concept of shared mobility, where postal operators use their widespread networks of units and serve as service providers. To enhance its real-world implementation, four sustainable transportation sharing practices are elaborated. The key question is how to identify the most sustainable alternative that should be offered by service providers. To resolve this challenge, this paper develops an advanced decision support model based on Aczel-Alsina aggregation operators and power operators within the intuitionistic fuzzy (IF) environment. The criteria weights are determined through the Shannon entropy-based power weighted method. Aczel-Alsina operations for IF numbers are proposed to aggregate the decision information. In light of these operational laws, two IF Aczel-Alsina aggregation operators and their enviable characteristics are provided. These advanced aggregation operators are used to formulate the IF power Aczel-Alsina model. The case study of the city of Novi Sad illustrates its applicability. According to the research findings, it is recommended that the public postal operator invests in a sharing e-bicycle fleet. The comparative investigation demonstrates the superiority of the developed decision support model. Its major strengths are simple calculation and fast information processing.

Institutional ‘Presence’ and the Indian State: The Long Narrative

The long durée narrative of the state in independent India is one of accumulation of incremental and aggregate power relations arrayed in and through political institutions inhabiting a field of power. These relationships are protean, exhibiting conflicts and contestations among institutions that compete for power within the domain of the state. The ‘ebbs’ and ‘flows’ in power are visible in the state-space represented by institutional arrangements and the patterns of relationship among them. While institutions perform the function of legitimation for the state, they also manifest the crisis that besets the state, reflecting dissonance between its moral–political goals of bringing about socio-economic transformation and its ability to achieve them through ‘state-bureaucratic’ practices. The constitutional state in India can be seen as the setting where the state-idea as limited by law and as an ‘instrument’ for bringing about transformative change was situated. Institutional crises during the emergency and the uneven trajectories of institutional presence in the ensuing period show the marginalization of institutions such as Parliament and the ambivalence of the judiciary in articulating the principles underlying the constitutional consensus. A dominative presence of the executive corroding deliberative spaces making them sites of adversarial combat and the populist appeal of the ‘leader’ drawing upon communitarian emotions has paved the way for a majoritarian state.

Resource aware pricing for electric vehicle charging

Electric vehicle charging facilities offer their capacity constrained electric charge and parking to users for a fee. As electric vehicle adoption grows, so too does the potential for excessive resource utilization. In this paper, we study how prices set by the charging facility impact the likelihood that specified resource utilization levels are exceeded. Specifically, we present probabilistic bounds on the number of charging spots and the total power supply needed at a facility based on the characteristics of the arriving vehicles. We assume the charging facility either offers a set of distinct and fixed charging rates to each user or allows the user to decide a charging deadline, from which a charging rate is determined. Users arrive randomly, requiring a random amount of charge. Additionally, each user has a random impatience factor that quantifies their value of time, and a random desired time to stay at a particular location. Assuming rational user behavior, and with knowledge of the probability distribution of the random parameters, we present high-confidence bounds on the total number of vehicles parked at the station and the aggregate power use of all vehicles actively charging. We demonstrate how these bounds can be used by a charging facility to determine appropriate pricing parameters and investigate through a Monte–Carlo simulation case study the tightness of the bounds.

Structural decomposition of heavy-duty diesel truck emission contribution based on trajectory mining

Nonlocal heavy-duty diesel trucks (HDDTs) transported across regions cause serious pollution in local atmospheric environments. However, previous studies misestimate the number of nonlocal HDDTs in the region and cannot identify the origin of the HDDTs on unmonitored road segments, resulting in unclear spatiotemporal distribution patterns of the emission contributions. This study therefore inferred the origins of HDDTs using trajectory data mining and obtained an accurate decomposition of the emission contribution from nonlocal HDDTs, from the single-vehicle-based HDDT emission inventory. A case study is conducted in Beijing. The results showed that the emission contribution of HDDTs from other regions to Beijing has a significant aggregation pattern and power function relationship with distance. The majority of nonlocal HDDTs in Beijing originate from Hebei and Tianjin and have the largest interaction intensity with Beijing, contributing 43% of the traffic counts and 37.05% of the emission intensity of HDDTs in Beijing. Temporally, the emission contribution of HDDTs from different regions has daily periodicity and is affected by major festivals. Nonlocal HDDTs dominate night-time emissions, which contributed 69.94% of total emissions at 1:00 a.m. The spatial heterogeneity of the emission contribution structure is mainly attributable to the traffic volume, highway freight ton-kilometers, and tertiary industry proportion. These findings extend the scientific understanding of the emission contributions of HDDTs and provide an important scientific basis for future strategies related to the control and management of emissions from HDDTs.

A Novel Data-Driven Method to Estimate Invisible Solar Power Generation: A Case Study in Taiwan

As the penetration of photovoltaic (PV) solar generation has increased, a significant number of residential and commercial solar PV systems have been installed without meters. The majority of these systems, however, are also not monitored by power system operators. Therefore, the uncertainty of the net load owing to this “invisible” solar power generation has raised additional challenges for power system operation. To reduce the impact of this, we propose a novel method to estimate the total solar power generation in a large region from a small representative subset. The proposed method is capable of capturing all relevant information that could assist in the identification of the representative subsets. Moreover, different optimization algorithms are utilized and evaluated to select the optimal number of clusters and representative subsets. As a case study, the power generation of 166 PV sites in Taiwan was collected and analyzed. The proposed method demonstrates a significant improvement in estimating aggregated power generation compared to other existing methods.

Distributed Peak Shaving for Small Aggregations of Cyclic Loads

Analogous to the way a good driver is aware of neighboring cars, electrical loads can coordinate with other loads within a building or section of a distribution grid. This paper develops methods that enable groups of cyclic loads (devices that turn on and off periodically to maintain setpoints) to reduce their peak aggregate power demand. The methods accommodate a wide variety of cyclic loads, including those with nonlinear or unknown dynamics, and can be implemented in a fully distributed fashion. This paper targets settings with a few hundred cyclic loads or fewer, where the methods developed here could reduce demand peaks significantly while maintaining or improving quality of service. This could save ratepayers money on monthly demand charges, decrease fuel use in microgrids, or extend the life of power delivery equipment.

An aggregation strategy of air conditioning loads considering diversity of the load

Through the reasonable aggregation and control of air conditioning loads (ACLs), the peak load of power grid can be reduced and the contradiction between supply and demand can be alleviated without affecting or less affecting the comfort of users. In order to further tap the demand response ability of aggregated loads, an aggregation strategy of ACLs considering diversity of the load is proposed. Firstly, grey relational analysis method is used to extract the thermal environmental characteristic parameters with high correlation to different categories of ACLs. Then, ACLs are classified by KNN algorithm; Finally, according to different control modes of ACLs, k-means algorithm is used to aggregate the load to obtain aggregated power. Through simulation verification, the aggregation strategy that retains the diversity of ACLs has high accuracy and can improve the aggregated active power capacity effectively. The strategy has higher practical application value.

CrowdIM: Crowd-Inspired Intelligent Manufacturing Space Design

Crowd-inspired intelligent manufacturing space (CrowdIM) aims to leverage the aggregated power of heterogeneous human–machine–things (HMT) agents for improving the efficiency of intelligent manufacturing. A significant scientific problem in CrowdIM is how to improve individual skills and crowd intelligence through cooperation, complementation, competition, and confrontation among HMT agents. The emergence mechanism of biological crowd intelligence provides an inspiration to address this challenge. This article explores the mapping mechanisms between natural crowd intelligence and CrowdIM, from the aspects, such as collective dynamics, self-adaptive mechanism, crowd intelligence optimization, graph structure mapping model, evolutionary game dynamics, multiagent learning, and so on. We further propose a general model of CrowdIM and expound it through a typical case study.

Dynamic DV-QKD Networking in Trusted-Node-Free Software-Defined Optical Networks

We demonstrate for the first time a four-node trusted-node-free metro network configuration with dynamic discrete-variable quantum key distribution DV-QKD networking capabilities across four optical network nodes. The network allows the dynamic deployment of any QKD link between two nodes of the network, while a QKD-aware centralised software-defined networking (SDN) controller is utilised to provide dynamicity in switching and rerouting. The feasibility of coexisting a quantum channel with carrier-grade classical optical channels where both the quantum and classical channels are in the C-band over field-deployed metropolitan networks and laboratory-based fibres (<10 km) is experimentally explored in terms of achievable quantum bit error rate, secret key rate as well as classical signal bit error rate. Moreover, coexistence analysis over multi-hops configuration using different switching scenarios is also presented. The secret key rate dropped 43% when coexisting one classical channel with 150 GHz spacing from the quantum channel for multiple links. This is due to the noise leakage from the Raman scattering into the 100 GHz bandwidth of the internal filter of the Bob DV-QKD unit. When coexisting four classical channels with 150 GHz spacing between quantum and the nearest classical channel, the quantum channel deteriorates faster due to the combination of Raman noise, other nonlinearities and high aggregated launch power causing the QBER value to exceed the threshold of 6% leading the SKR to reach a value of zero bps at a launch power of 7 dB per channel. Furthermore, the coexistence of a quantum channel and six classical channels through a field-deployed fibre test network is examined.

Simultaneous generation and network expansion planning in large‐scale power systems under exact AC power flow equations

This article presents a novel three-step structure for simultaneous generation and network expansion planning under the exact AC power flow equations. This tractable structure allows for detailed investment decisions on new generation and network facilities. In the first step of the proposed coordinated planning framework, a power system is partitioned into regions based on electrical and geographical distances. Network aggregation in the first step is achieved by a new linear clustering model ameliorating both computational efficiency and accuracy. In the second step, the aggregated power system with a specific number of regions is cost-effectively expanded on a regional basis considering inter-regional power flows. In the third step, new generation and network facilities imposed by the second step of the proposed coordinated planning framework are allocated in each region. Moreover, intra-regional systems are cost-effectively expanded by new transmission lines and transformers. The proposed approach is implemented on IEEE 30-bus, 39-bus, 300-bus, and 1,354-bus test systems. The simulation results illustrate its high advantages from accuracy, practicality, and tractability points of view.

OpenComm: Open community platform for data integration and privacy preserving for 311 calls

Local governments are increasingly leveraging administrative data to drive performance. Likewise, cities are interested in improving responsiveness to citizens’ demands and cost savings through data analytics. However, city managers face many challenges when utilizing secondary data, such as 311 call records and the US Census. The challenge of interest to the current study is boundary issues as a result of data being collected at divergent geographic levels over different time horizons. Accordingly, an inductive analytical methodology was developed to create units of analysis that were both pragmatically and analytically appropriate for city managers and local policymakers. We created an open data analytics framework called OpenComm to harmonize administrative and secondary data using administrative data derived from Kansas City, Missouri. This framework produced robust inferences regarding the spatial and temporal aspects for the communities. Privacy-preserving technology, in particular, has been applied to public data to protect community privacy. The findings illustrate the power of inductive data aggregation, leading to empirical insights into hidden patterns of city service disparity over a decade-long time horizon. An application for the Open Data Platform is available at http://kc311.herokuapp.com/.