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Batteries can provide valuable operational flexibility to facilitate the system efficiency. However, there lacks market environments to effectively harness and monetize the value of these assets. Most existing works apply a profit-oriented single-signal pricing scheme, which mixes the value of energy and flexibility together and may ultimately raise the electricity bill. Therefore, this paper proposes a profit-neutral double-signal retail pricing scheme that distinguishes elastic market players (i.e., batteries) from inelastic market players (i.e., inflexible loads) and quantifies the value of energy and flexibility separately. Experimental results indicate that under the incentive provided by the proposed retail pricing scheme: 1) The system efficiency benefit aligns with benefits to both batteries and inflexible loads; 2) Value of operational flexibility, contributing to improve the energy efficiency, can be transparently priced and fairly allocated among batteries; and 3) The dominant role in which inflexible loads play on determining the market price is avoided.

In this article, an optimal strategy is proposed for heating, ventilation, and air conditioning (HVAC) systems in commercial buildings to fairly ensure the occupants’ comfort, while participating in frequency regulation. With the nonlinear relationship between temperature setpoint and the fan power, the differences between indoor temperatures and temperature setpoints are considered as the objective to fairly ensure the occupants’ comfort. By the proposed strategy, the task for frequency regulation is optimally distributed according to the capacity of HVAC systems in a load aggregator. Therefore, performing frequency regulation and fairly ensuring occupants’ comfort are achieved simultaneously. Simulations on a two-area interconnected power system show that the proposed optimal strategy can improve the quality of frequency, reduce traditional generator regulation, and ensure the occupants’ comfort fairly.

The power consumption of inverter air conditioners (IACs) can be regulated flexibly by adjusting the compressor's operating frequency, which has been proven suitable for providing regulation capacities to power systems. Considering the rapid phasing out of traditional generating units, massive IACs create huge alternative regulation potential. However, the impact of IACs on the power system's stability is rarely studied. To address this issue, this article proposes the modeling and control methods of IACs to provide regulation capacities to power systems. On this basis, a novel power system model with the control loop of large-scale IACs is developed, where the communication latency during the control signal transfer process is also considered. Then, the dynamic performance and steady-state errors of the novel power system are evaluated, showing that IACs can quickly participate in and smoothly withdraw from the regulation process. Furthermore, the stabilities and sensitivities of power systems with and without IACs are compared, in order to illustrate that both the stability margin and robustness of the power system can be increased via the control loop of IACs. Finally, the effectiveness of the proposed models and methods are verified by numerical studies.

In the transition to a society with net-zero carbon emissions, high penetration of distributed renewable power generation and large-scale electrification of transportation and heat are driving the conventional distribution network operators (DNOs) to evolve into distribution system operators (DSOs) that manage distribution networks in a more active and flexible way. As a radical decentralized data management technology, distributed ledger technology (DLT) has the potential to support a trustworthy digital infrastructure facilitating the DNO–DSO transition. Based on a comprehensive review of worldwide research and practice, as well as the engagement of relevant industrial experts, the application of DLT in distribution networks is identified and analyzed in this article. The DLT features and DSO needs are first summarized, and the mapping relationship between them is identified. Detailed DSO functions are identified and classified into five categories (i.e.“, planning”, “operation”, “market”, “asset”, and “connection”) with the potential of applying DLT to various DSO functions assessed. Finally, the development of seven key DSO functions with high DLT potential is analyzed and discussed from the technical, legal, and social perspectives, including peer-to-peer energy trading, flexibility market facilitation, electric vehicle charging, network pricing, distributed generation register, data access, and investment planning.

Buildings, as major energy consumers, can provide great untapped demand response (DR) resources for grid services. However, their participation remains low in real-life. One major impediment for popularizing DR in buildings is the lack of cost-effective automation systems that can be widely adopted. Existing optimization-based smart building control algorithms suffer from high costs on both building-specific modeling and on-demand computing resources. To tackle these issues, this paper proposes a cost-effective edge-cloud integrated solution using reinforcement learning (RL). Beside RL’s ability to solve sequential optimal decision-making problems, its adaptability to easy-to-obtain building models and the off-line learning feature are likely to reduce the controller’s implementation cost. Using a surrogate building model learned automatically from building operation data, an RL agent learns an optimal control policy on cloud infrastructure, and the policy is then distributed to edge devices for execution. Simulation results demonstrate the control efficacy and the learning efficiency in buildings of different sizes. A preliminary cost analysis on a 4-zone commercial building shows the annual cost for optimal policy training is only 2.25% of the DR incentive received. Results of this study show a possible approach with higher return on investment for buildings to participate in DR programs.

This paper improves the performance of the revised-simplified verifiable re-encryption mixnet (R-SVRM) based e-voting scheme by introducing confirmation numbers ( CN s) that are used in the CN based e-voting scheme. Although CN based and R-SVRM based schemes had made e-voting schemes more practical by excluding zero knowledge proof (ZKP) that requires large volume of computations, still they are not efficient enough. Namely, the CN based scheme adopts RSA encryption functions that are not probabilistic or commutative, therefore to satisfy essential requirements of elections, extra random factors are necessary for individual votes, election authorities must sign on votes and they must keep encryption keys as their secrets. On the other hand, although the R-SVRM based scheme uses ElGamal encryption functions that are probabilistic and commutative, vote forms in it is complicated, i.e. , vote forms consist of at least 3 items and they include information about candidates as exponents. The improved scheme simplifies vote forms by exploiting CN s, and extensively reduces the number of operations required for individual votes. Also, the scheme successfully satisfies all essential requirements of e-voting systems, i.e. , it is endowed with features about privacy, robustness, accuracy, incoercibility and fairness as CN based and R-SVRM based schemes are.

The integration of unmanned aerial vehicles (UAVs) into spectrum sensing cognitive communication networks can offer many benefits for massive connectivity services in 5G communications and beyond; hence, this work analyses the performance of non-orthogonal multiple access-based cognitive UAV-assisted ultra-reliable and low-latency communications (URLLCs) and massive machine-type communication (mMTC) services. An mMTC service requires better energy efficiency and connection probability, whereas a URLLC service requires minimising the latency. In particular, a cognitive UAV operates as an aerial secondary transmitter to a ground base station by sharing the unlicensed wireless spectrum. To address these issues, we derive the analytical expressions of throughput, energy efficiency, and latency for mMTC/URLLC-UAV device. We also formulate an optimisation problem of energy efficiency maximisation to satisfy the needs of URLLC latency and mMTC throughput and solve it using the Lagrangian method and the Karush-Kuhn-Tucker conditions. The algorithm is presented by jointly optimising the transmission powers of the mMTC and URLLC users. The derived expressions and algorithm are then used to evaluate the performance of the proposed system model. The numerical results show that the proposed algorithm improves the energy efficiency and satisfies the latency requirement of the mMTC/URLLC-UAV device.

A protuberant issue of the present time is that, organizations from different domains are struggling to obtain effective solutions for detecting online-based fake news. It is quite thought-provoking to distinguish fake information on the internet as it is often written to deceive users. Compared with many machine learning techniques, deep learning-based techniques are capable of detecting fake news more accurately. Previous review papers were based on data mining and machine learning techniques, scarcely exploring the deep learning techniques for fake news detection. However, emerging deep learning-based approaches such as Attention, Generative Adversarial Networks, and Bidirectional Encoder Representations for Transformers are absent from previous surveys. This study attempts to investigate advanced and state-of-the-art fake news detection mechanisms pensively. We begin with highlighting the fake news consequences. Then, we proceed with the discussion on the dataset used in previous research and their NLP techniques. A comprehensive overview of deep learning-based techniques has been bestowed to organize representative methods into various categories. The prominent evaluation metrics in fake news detection are also discussed. Nevertheless, we suggest further recommendations to improve fake news detection mechanisms in future research directions.

A detailed analysis of how Grid-interactive Efficient Buildings (GEB) can participate as active elements in a microgrid through on-site PV electricity generation and energy efficiency applications is presented. A case study using three US Department of Energy (DoE)-developed prototype commercial building models are used. These represent a secondary school, a hospital and a large office building. Simulation results show that when schools, hospitals and office buildings are operated as GEBs, there are always electricity savings, but savings amounts vary depending on levels of HVAC and lighting controls within the limits of customer comfort levels. These comfort level ranges are determined through interactions with building occupants which resulted in $\Delta \text{T}$ of 2- $5^{\circ }\text{F}$ and dimming level range of 20% to 50%. Savings in the school building are so much higher for two reasons. One, because without GEB application these buildings are operated in a business-as-usual fashion throughout the year, even when the school is not in session. The second reason is – being a two-story building the roof area is comparatively much higher than the hospital or the multi-storied office buildings.

Residential HVAC control is a large untapped resource for providing regulation services to the grid. This paper presents a set of algorithms for controlling HVACs of a group of residential houses that a demand response aggregator can use to sell regulation service in the wholesale market. The focus is on the regulation market offered by the PJM RTO. Real-world regulation signals from PJM are used to simulate the performance and range of regulation services in a realistic scenario. After presenting the empirical counter example for why a universal optimal control strategy cannot exist for regulation, a set of heuristic algorithms is presented, which performs well in a range of test cases. The control mechanism involves a central controller communicating with smart thermostats of multiple residential houses to gather indoor temperature data, prioritizing them according to certain heuristics and sending on/off signals back to the thermostats to control the HVAC. The case studies indicate that the proposed heuristic algorithms can deliver the required regulation services, while adequately handling communication delays, different types of regulation signals and household’s thermal comfort requirements.

With the development of the Internet of Things (IoT) technologies, implementing intelligent controls in buildings to reduce energy consumption is becoming increasingly popular. Building climate control is of strong research interest due to high energy savings potentials associated with heating, ventilation, and air-conditioning (HVAC) controls. Because the operation of HVAC systems directly influences occupant thermal comfort, building-specific thermal models are needed for proper control. Such models describe how indoor temperature changes under different environmental conditions and HVAC status. Previous studies mainly developed thermal models for accurate building thermal simulation which are not practical for use in real-world applications as they require domain knowledge and lengthy building-by-building configuration. In this article, taking advantage of IoT technologies, a plug-and-play learning framework is proposed to automatically identify the thermal model of each thermal zone in a building without manual configuration. In contrast to the existing methods, a thermal model is learned using low-resolution temperature readings from IoT-based smart thermostats. The learning framework has been validated using the data collected from an IoT platform installed in a building over a summer. The performance of the learned model and the error of indoor temperature prediction based on this model have been evaluated and quantified. Findings demonstrate the learning process can be automated with either edge-computing or cloud-computing. This article shows the validity of the proposed IoT-based thermal model learning framework and offers a pragmatic solution for providing reliable thermal models to future smart building climate controls.

Given the considerable scale of distribution networks in urban and rural areas, as well as the lack of management records, adjustments of switches during the distribution system operation are poorly documented. Such deficiency results in the inaccuracy of models stored in the distribution network automation system, and thus misleads the state estimation. With the emergence of information and communication technology, a large number of the feeder and residential smart meter data are accumulated. Such data can help recognize the operation modes of distribution networks by analyzing the relationships between the on/off states of switches and the voltage correlations among buses. However, the limited quantity and quality of the sampling data restrict the implementation of data-driven recognition. In this paper, a physical-probabilistic-network (PPN) model applied for inferring overall operation mode of distribution networks is proposed. Based on which, a belief propagation-based algorithm is proposed for the inference even under situations when there are only partial bus voltages data available. Meanwhile, the required variable for inference can be reduced from the active trail analysis. Experiment results are used to compare its performance with classic methods and to prove its effectiveness and advantages.

Virtual inertia control is considered as an important part of microgrids with high renewable penetration. Virtual inertia emulation based on the derivative of frequency is one of the effective methods for improving system inertia and maintaining frequency stability. However, in this method, the ability to provide virtual damping is usually neglected in its design, and hence, its performance might be insufficient in the system with low damping. Confronted with this issue, this paper proposes a novel design and analysis of virtual inertia control to imitate damping and inertia properties simultaneously to the microgrid, enhancing frequency performance and stability. The proposed virtual inertia control uses the derivative technique to calculate the derivative of frequency for virtual inertia emulation. Trajectory sensitivities have been performed to analyze the dynamic impacts of the virtual inertia and virtual damping variables over the system performance. Time-domain simulations are also presented to evaluate the efficiency of the virtual damping and virtual inertia in enhancing system frequency stability. Finally, the efficiency and robustness of the proposed control technique are compared with the conventional inertia control under a wide range of system operation, including the decrease in system damping and inertia and high integrations of load variation and renewable energy.

This article provides an overview of key features pertaining to CSI reporting and beam management for the 5G New Radio (NR) currently being standardized in 3GPP. For CSI reporting, the modular design framework and high-resolution spatial information feedback offer not only flexibility in a host of use cases and deployment scenarios, but also improved average user throughput over state-of-the-art 4G LTE. To accommodate cellular communications in the milimeter-wave regime where a combination of analog and digital beamforming is typically used at both a base station and user equipment, beam management procedures such as measurement, reporting, and recovery are introduced. The utility and joint usage of these two features are demonstrated along with some potential upgrades for the next phase of 5G NR. Introduction

In this paper, we propose techniques for dynamically adjusting heartbeat or keep-alive interval of long-lived TCP connections, particularly the ones that are used in push notification service in mobile platforms. When a device connects to a server using TCP, often times the connection is established through some sort of middle-box, such as NAT, proxy, firewall, and so on. When such a connection is idle for a long time, it may get torn down due to binding timeout of the middle-box. To keep the connection alive, the client device needs to send keep-alive packets through the connection when it is otherwise idle. To reduce resource consumption, the keep-alive packet should preferably be sent at the farthest possible time within the binding timeout. Due to varied settings of different network equipments, the binding timeout will not be identical in different networks. Hence, the heartbeat rate used in different networks should be changed dynamically. We propose a set of iterative probing techniques, namely binary, exponential, and composite search, that detect the middle-box binding timeout with varying degree of accuracy; and in the process, keeps improving the keep-alive interval used by the client device. We also analytically derive performance bounds of these techniques. To the best of our knowledge, ours is the first work that systematically studies several techniques to dynamically improve keep-alive interval. To this end, we run experiments in simulation as well as make a real implementation on android to demonstrate the proof-of-concept of the proposed schemes.

Curtailed wind energy is a challenge in utilities with high wind energy penetration. This happens mainly when wind generation exceeds load minus the minimum stable operating point of generation units. At first, the role of generation mix on the curtailed wind energy is analyzed. Then, demand response (DR) applications are modeled to quantify additional reductions in the curtailed wind energy. The uniqueness of this approach is that the impact of the DR rebound effect on the system load shape can be directly reflected. This allows scheduling of DR such that the rebound period (higher demand) aligns itself with high wind output period. This minimizes the need for curtailment. Next, the issue of charging/discharging different large-scale energy storage technologies (e.g., compressed air energy storage, pumped hydro energy storage, and batteries) is addressed. This allows their impact on wind curtailment to be analyzed in detail. Finally, various combinations of energy storage and DR options are considered for investigating their impacts on further reducing wind curtailments. This problem is formulated as a mixed integer linear programming using wind and load data from Bonneville Power Administration.

This paper presents robust virtual inertia control of an islanded microgrid considering high penetration of renewable energy sources (RESs). In such microgrids, the lack of system inertia due to the replacement of traditional generating units with a large amount of RESs causes undesirable influence to microgrid frequency stability, leading to weakening of the microgrid. In order to handle this challenge, the $H_{\mathbf {\infty }}$ robust control method is implemented to the virtual inertial control loop, taking into account the high penetration of RESs, thus enhancing the robust performance and stability of the microgrid during contingencies. The controller’s robustness and performance are determined along with numerous disturbances and parametric uncertainties. The comparative study between $H_{\mathbf {\infty }}$ and optimal proportional-integral (PI)-based virtual inertia controller is also presented. The results show the superior robustness and control effect of the proposed $H_{\mathbf {\infty }}$ controller in terms of precise reference frequency tracking and disturbance attenuation over the optimal PI controller. It is validated that the proposed $H_{\mathbf {\infty }}$ -based virtual inertia controller successfully provides desired robust frequency support to a low-inertia islanded microgrid against high RESs penetration.

The future wireless network, such as the Centralized Radio Access Network (C-RAN), will need to deliver data rate about 100–1000 times the current fourth-generation (4G) technology. For the C-RAN-based network architecture, there is a pressing need for tremendous enhancement of the effective data rate of the common public radio interface (CPRI). Compression of CPRI data is one of the potential enhancements. In this paper, we introduce a vector quantization based compression algorithm for CPRI links, utilizing the Lloyd algorithm. Methods to vectorize the I/Q samples and enhanced initialization of the Lloyd algorithm for codebook training are investigated for improved performance. Multistage vector quantization and unequally protected multigroup quantization are considered to reduce codebook search complexity and codebook size. Simulation results show that our solution can achieve compression of four times for uplink and 4.5 times for downlink, within $2\%$ error vector magnitude (EVM) distortion. Remarkably, vector quantization codebook proves to be quite robust against data modulation mismatch, fading, signal-to-noise ratio (SNR), and Doppler spread.

An important step toward a smart grid is the “smart” application of manual or automated sectionalizing switches, which can be operated in contingency conditions in order to improve the reliability indices. However, electric utilities do not have well-defined criteria for allocation of such devices. In this paper, annual financial losses related to process trips (AFLPT) are assessed taking into account—in a probabilistic manner—process immunity time (PIT) of each customer and the restoration duration of customers located upstream and downstream of the faulted section. AFLPT is used as an economic index along with collective reliability indices to select a better tradeoff between benefit and investment cost during the allocation of automated sectionalizing switches. Results show that the correlation between PIT and restoration duration allows electric utilities to identify geographic areas where economic investment in modern technologies, as self-healing, can be prioritized.

Distribution voltage profiles are subjected to overvoltage limit violations from high penetration of grid-connected photovoltaic (PV) systems. Such voltage rises seen at the point of PV interconnection can be mitigated by adaptively changing the active and/or reactive power injection from the PV inverter. This work proposes a local voltage regulation technique that utilizes very short-term (15 s) PV power forecasts to circumvent imminent upper voltage limit violation or an overvoltage scenario. To provide these PV generation forecasts, a hybrid forecasting method is formulated based on Kalman filter theory, which applies physical PV generation modeling using high-resolution (15 s) data from on-site measurements. The proposed algorithm employs an active power curtailment based on these PV power forecasts, when the reactive power estimate given by a droop-based method cannot provide the desired voltage regulation within predefined power factor limits. The curtailment threshold values are calculated in such a way that this voltage regulation technique can reduce possible voltage limit violations. The effectiveness of the proposed method is demonstrated with case studies developed on a standard test feeder with realistic load and PV generation profiles.

We consider a video delivery problem in which a server multicasts a video to several users over a wireless broadcast channel. Users arrive and depart asynchronously, and when a user arrives, he/she wishes to watch the video from the beginning. Existing approaches for this problem assume that the server is either completely aware of the state of the users or is completely oblivious to their states, the former being most useful for small numbers of users and the latter being most useful for large networks. We propose a dynamic-programming-based scheduler that can operate in both of these regimes, as well as the intermediate regime in which feedback is available but incomplete. The scheduler is validated both numerically and experimentally.

This paper presents a robust control design to integrate hydro generators to address increased frequency deviations in power systems with high wind power penetration. A robust control design framework for hydro governors is proposed in order to particularly tackle with disturbances from wind power fluctuation. First, by combining state equations of hydro, thermal, and wind generators through the power flow equations of network, a state space model of the entire power system is derived for the control design of hydro generators. In particular, the characteristics of wind power variations and the hydraulic system dynamics of hydroelectric power plants are implemented in the system model. Then, the $H_{\infty }$ -based robust controller is synthesized by reducing the order of the system model and using the linear matrix inequality method. Finally, the IEEE 39-bus test system is used to verify the performance of the designed robust controller with 10–40% wind penetration levels. Simulation results are compared with a traditional PID controller. Research findings indicate that the proposed robust controller significantly reduces system frequency deviations and enables hydro generators to be more responsive to wind power variation in primary frequency regulation of power systems.

The objective of this paper is to propose an approach for smart distribution transformer (TR) management to help implement emergency demand response (DR) at a TR. In this paper, a DR algorithm embedded in a multiagent system is presented. The proposed algorithm ensures that an instantaneous power demand at a TR is kept below a certain demand limit during a DR event while the impacts of demand restrike are minimized. At the same time, homeowners' goals of securing critical loads, mitigating comfort level violation, and minimizing appliance's compensation time during a DR event are taken into account. To validate and evaluate the effectiveness of the proposed DR algorithm, a case study of a TR serving three homes is demonstrated.

We determine the rate region of the vector Gaussian one-helper source-coding problem under a covariance matrix distortion constraint. The rate region is achieved by a simple scheme that separates the lossy vector quantization from the lossless spatial compression. The converse is established by extending and combining three analysis techniques that have been employed in the past to obtain partial results for the problem., Comment: 36 pages; 2 figures; submitted to IEEE Transactions on Information Theory; material in this paper was presented in part at the 49th Annual Allerton Conference on Communications, Control, and Computing, University of Illinois, Urbana-Champaign, Sept. 2011

Electrical power consumption data and load profiles of major household appliances are crucial elements for demand response studies. This paper discusses load profiles of selected major household appliances in the U.S., including two clothes washers, two clothes dryers, two air conditioners, an electric water heater, an electric oven, a dishwasher, and two refrigerators. Their electrical power consumption data measured in one-second intervals, together with one-minute data (averaged over 60 one-second readings), are provided in an online data repository (URL: www.ari.vt.edu/research-data/). The data were gathered from two homes in Virginia and Maryland during July-October 2012. In this paper, demand response opportunities provided by these appliances are also discussed.