Achieving the UN's sustainable energy targets through dynamic operating limits.

Despite the world's relentless efforts to achieve the United Nations' sustainable energy target by 2030, the current pace of progress is insufficient to reach the objective. Continuous support and development across various domains of the energy sector are required to achieve sustainability targets. This article focuses on the potential of dynamic operating limits to drive the world's sustainability efforts, specifically in addressing critical challenges of distribution networks of the power system by progressively setting the nodal limits on the active and reactive power injection into the distribution network based on data-driven computer simulation. While the importance of dynamic operating limits has recently been recognized, its crucial role in the residential energy sustainability sector, which requires a significant push to provide universal energy access by 2030, has not been adequately investigated. This perspective explains the fundamental concepts and benefits of dynamic operating limits in encouraging the adoption of distributed renewable energy resources in the residential sector to support the United Nation's sustainable energy objective. Additionally, we discuss the limitations of computing this limit and applying it to the electricity network and some motivational models that can encourage electricity customers to come forward to address the challenges. Finally, we explore new research and implementation prospects for designing comprehensive, dependable, accountable, and complementary dynamic operating limit programs to accelerate the attainment of sustainable energy targets.

Slow-Varying Dynamics-Assisted Temporal Capsule Network for Machinery Remaining Useful Life Estimation.

Capsule network (CapsNet) acts as a promising alternative to the typical convolutional neural network, which is the dominant network to develop the remaining useful life (RUL) estimation models for mechanical equipment. Although CapsNet comes with an impressive ability to represent entities' hierarchical relationships through a high-dimensional vector embedding, it fails to capture the long-term temporal correlation of run-to-failure time series measured from degraded mechanical equipment. On the other hand, the slow-varying dynamics, which reveals the low-frequency information hidden in mechanical dynamical behavior, is overlooked in the existing RUL estimation models (including CapsNet), limiting the utmost ability of advanced networks. To address the aforementioned concerns, we propose a slow-varying dynamics-assisted temporal CapsNet (SD-TemCapsNet) to simultaneously learn the slow-varying dynamics and temporal dynamics from measurements for accurate RUL estimation. First, in light of the sensitivity of fault evolution, slow-varying features are decomposed from normal raw data to convey the low-frequency components corresponding to the system dynamics. Next, the long short-term memory (LSTM) mechanism is introduced into CapsNet to capture the temporal correlation of time series. To this end, experiments conducted on an aircraft engine and a milling machine verify that the proposed SD-TemCapsNet outperforms the mainstream methods. In comparison with CapsNet, the estimation accuracy of the aircraft engine with four different scenarios has been improved by 10.17%, 24.97%, 3.25%, and 13.03% about the index root mean squared error, respectively. Similarly, the estimation accuracy of the milling machine has been improved by 23.57% compared to LSTM and 19.54% compared to CapsNet.

A Survey of Blind Modulation Classification Techniques for OFDM Signals.

Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communications to improve spectral efficiency and power efficiency, and reduce latency. It will become a integral part of intelligent software-defined radios (SDR) for future communication. In this paper, we provide various MC techniques for orthogonal frequency division multiplexing (OFDM) signals in a systematic way. We focus on the most widely used statistical and machine learning (ML) models and emphasize their advantages and limitations. The statistical-based blind MC includes likelihood-based (LB), maximum a posteriori (MAP) and feature-based methods (FB). The ML-based automated MC includes k-nearest neighbors (KNN), support vector machine (SVM), decision trees (DTs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) based MC methods. This survey will help the reader to understand the main characteristics of each technique, their advantages and disadvantages. We have also simulated some primary methods, i.e., statistical- and ML-based algorithms, under various constraints, which allows a fair comparison among different methodologies. The overall system performance in terms bit error rate (BER) in the presence of MC is also provided. We also provide a survey of some practical experiment works carried out through National Instrument hardware over an indoor propagation environment. In the end, open problems and possible directions for blind MC research are briefly discussed.

Roles of retailers in the peer-to-peer electricity market: A single retailer perspective.

Despite extensive research in the past five years and several successfully completed and on-going pilot projects, regulators are still reluctant to implement peer-to-peer trading at a large scale in today's electricity market. The reason could partly be attributed to the perceived disadvantage of current market participants such as retailers due to their exclusion from market participation-a fundamental property of decentralized peer-to-peer trading. As a consequence, recently, there has been growing pressure from energy service providers in favor of retailers' participation in peer-to-peer trading. However, the role of retailers in the peer-to-peer market is yet to be established, as no existing study has challenged this fundamental circumspection of decentralized trading. In this context, this perspective takes the first step to discuss the feasibility of retailers' involvement in the peer-to-peer market. In doing so, we identify key characteristics of retail-based and peer-to-peer electricity markets and discuss our viewpoint on how to incorporate a single retailer in a peer-to-peer market without compromising the fundamental decision-making characteristics of both markets. Finally, we give an example of a hypothetical business model to demonstrate how a retailer can be a part of a peer-to-peer market with a promise of collective benefits for the participants.

Smart Distribution Boards (Smart DB), Non-Intrusive Load Monitoring (NILM) for Load Device Appliance Signature Identification and Smart Sockets for Grid Demand Management.

Traditionally, the choices to balance the grid and meet its peaking power needs are by installing more spinning reserves or perform load shedding when it becomes too much. This problem becomes worse as more intermittent renewable energy resources are installed, forming a substantial amount of total capacity. Advancements in Energy Storage System (ESS) provides the utility new ways to balance the grid and to meet its peak demand by storing un-used off peak energy for peak usage. Large sized ESS-mega watt (MW) level-are installed by different utilities at their substations to provide the high speed grid stabilization to balance the grid to avoid installing more capacity or triggering any current load shedding schemes. However, such large sized ESS systems and their required inverters are costly to install, require much space and their efficacy could also be limited due to network fault current limits and impedances. In this paper, we propose a novel approach and trial for 3000+ homes in Singapore of achieving a large capacity of demand management by developing a smart distribution board (DB) in each home with the high speed metering sensors (>6 kHz sampling rate) and non-intrusive load monitoring (NILM) algorithm, that can assist home users to perform the load/appliance profile identification with daily usage patterns and allow targeted load interruption using the smart sockets/plugs provided. By allowing load shedding at device or appliance level, while knowing their usage profile and preferences, this can allow such an approach to become part of a new voluntary interruptible load management system (ILMS) that requires little user intervention, while minimizing disruption to them, allowing ease of mass participation and thus achieving the intended MW demand management capacities for the grid. This allows for a more cost effective way to better balance the grid without the need for generation capacity growth, large ESS investment while improving the way to perform load shedding without disruptions to entire districts. Simply, home users can now know and participate with the grid in interruptible load (IL) schemes to target specific home appliance, such as water heaters or air conditioning, allowing interruptions during certain times of the day, instead of the entire house, albeit with the right incentives. This allows utilities to achieve MW capacity load shedding with millions of appliances with their preferences, and most importantly, with minimal disruptions to their consumers quality of life. In our paper, we will also consider coupling a small sized Home Energy Storage System (HESS) to amplify the demand management capacity. The proposed approach does not require any infrastructure or wiring changes and is highly scalable. Simulation results demonstrate the effectiveness of the NILM algorithm and achieving high capacity grid demand management. This approach of taking user preferences for appliance level load shedding was developed from the results of a survey of 500 households that indicates >95% participation if they were able to control their choices, possibly allowing this design to be the most successful demand management program than any large ESS solution for the utility. The proposed system has the ability to operate in centralized as part of a larger Energy Management System (EMS) Supervisory Control And Data Acquisition (SCADA) that decide what to dispatch as well as in autonomous modes making it simpler to manage than any MW level large ESS setup. With the availability of high-speed sampling at the DB level, it can rely on EMS SCADA dispatch or when disconnected, rely on the decaying of the grid frequency measured at the metering point in the Smart DB. Our simulation results demonstrate the effectiveness of our proposed approach for fast grid balancing.

Layered Coding for Mobile Cloud Gaming Using Scalable Blinn-Phong Lighting.

In a mobile cloud gaming, high-quality, high-frame-rate game images of immense data size need to be delivered to the clients over wireless networks under stringent delay requirement. For good gaming experience, reducing the transmission bit rate of the game images is necessary. Most existing cloud gaming platforms simply employ standard, off-the-shelf video codecs for game image compression. In this paper, we propose the layered coding scheme to reduce transmission bandwidth and latency. We leverage the rendering computation of modern mobile devices to render a low-quality local game image, or the base layer (BL). Instead of sending a high-quality game image, cloud servers can send enhancement layer information, which clients can utilize to improve the quality of the BL. Central to the layered coding scheme is the design of a complexity-scalable BL rendering pipeline that can be executed on a range of power-constrained mobile devices. In this paper, we focus on the lighting stage in modern graphics rendering and propose a method to scale the popular Blinn-Phong lighting for the use in BL rendering. We derive an information-theoretic model on the Blinn-Phong lighting to estimate the rendered image entropy. The analytic model informs the optimal BL rendering design that can lead to maximum bandwidth saving subject to the constraint on the computation capability of the client. We show that the information rate of the enhancement layer could be much less than that of the high-quality game image, while the BL can be generated with only a very small amount of computation. Experiment results suggest that our analytic model is accurate in estimating. For layered coding scheme, up to 84% reduction in bandwidth usage can be achieved by sending the enhancement layer information instead of the original high-quality game images compressed by H.264/AVC.