We present a novel switched-capacitor, integrator-multiplexing, second-order delta-sigma modulator (DSM) featuring a single differential difference amplifier (DDA). Power consumption is low and resolution is high when this DSM is used for portable electroencephalographic applications. A single DDA (rather than a conventional operational transconductance amplifier) with appropriate switch and capacitor architectures is used to create the second-order switched-capacitor DSM. The configuration ensures that the resolution is high. The modulator was implemented using a standard 180nm complementary metal-oxide-silicon process. At a supply voltage of 1.8V, a signal bandwidth of 250Hz and a sampling frequency of 200kHz, simulations demonstrated that the modulator achieved an 82dB peak signal-to-noise-distortion ratio and an effective number of bits of 14.

Smart residential communities tend to play an important role in demand response technologies due to their greater DR potential compared to single homes in smart grids. However, the heterogeneity in energy consumption between residents in a community leads to the low effectiveness of one-size-fits-all DR scheduling for a single home. To reflect the heterogeneity of energy consumption among residents and realize the demand response scheduling for a community, a centralized DR scheduling algorithm was presented. It introduces the willingness to pay to quantify the heterogeneity of residents' energy consumption by making full use of fuzzy clustering. Then, the quantized willingness to pay is applied to a Nash equilibrium game framework to maximize the individual surplus of residents, better adapting to all the demands of the residents in the community. Simulation results show that compared with the demand response scheduling algorithm without WTP, the presented demand response scheduling algorithm with WTP can reduce the amount of transferred data by about 30%. Compared with a demand response algorithm using game theory, the presented demand response scheduling algorithm can reduce the energy cost of the community by about 10% and satisfy the diversified demands of the residents while maintaining a small peaking-to-average ratio.(c) 2022 Elsevier Science. All rights reserved. (c) 2022 Elsevier B.V. All rights reserved.

Advances in information and communication technologies have significantly influenced the operation of low-voltage distribution grids. As essential elements of distribution grids, user-side smart meters find many smart grid applications, for example to measure electrical energy use and facilitate communications. However, the service models of distribution grids remain under development in association with upgrading of user-side smart meters. These meters are resource constrained, and challenging to upgrade on a large scale. To address this issue, this article describes a container-driven service architecture, in which containers are used to create a virtual dedicated agent (digital twin) for each user-side smart meter. The agent can be deployed either in the cloud or on an edge system, and can be upgraded to support emerging smart grid applications, thus minimizing the future upgrading requirements of user-side smart meters. We built experimental test beds to verify the proposed architecture and evaluated its performance in real-world experiments.

As a distributed computing paradigm, edge computing has become a key technology for providing timely services to mobile devices by connecting Internet of Things (IoT), cloud centers, and other facilities. By offloading compute-intensive tasks from IoT devices to edge/cloud servers, the communication and computation pressure caused by the massive data in Industrial IoT can be effectively reduced. In the process of computation offloading in edge computing, it is critical to dynamically make optimal offloading decisions to minimize the delay and energy consumption spent on the devices. Although there are a large number of task offloading-decision models, how to measure and evaluate the quality of different models and configurations is crucial. In this article, we propose a novel simulation platform named ChainFL, which can build an edge computing environment among IoT devices while being compatible with federated learning and blockchain technologies to better support the embedding of security-focused offloading algorithms. ChainFL is lightweight and compatible, and it can quickly build complex network environments by connecting devices of different architectures. Moreover, due to its distributed nature, ChainFL can also be deployed as a federated learning platform across multiple devices to enable federated learning with high security due to its embedded blockchain. Finally, we validate the versatility and effectiveness of ChainFL by embedding a complex offloading-decision model in the platform, and deploying it in an Industrial IoT environment with security risks.

The fluctuations in electricity prices and intermittency of renewable energy systems necessitate the adoption of online energy management schemes in industrial microgrids. However, it is challenging to design effective and optimal online rolling horizon energy management strategies that can deliver assured optimality, subject to the uncertainties of volatile electricity prices and stochastic renewable resources. This paper presents an adaptable online energy management scheme for industrial microgrids that minimizes electricity costs while meeting production requirements by repeatedly solving an optimization problem over a moving control window, taking advantage of forecasted future prices and renewable energy profiles implemented by a hybrid deep learning model. The predicted values over the control horizon are assumed to be uncertain, and a multivariate Gaussian distribution is used to handle the variations in electricity prices and renewable resources around their predicted nominal values. Simulation results under different scenarios using real-world data verify the effectiveness of the proposed online energy management scheme, assessed by the corresponding gaps with respect to several selected benchmark strategies and the ideal boundaries of the best and worst known solutions. Furthermore, the robustness of the scheme is verified by considering severe errors in forecasted electricity prices and renewable profiles.

Aged care communities have been under the spotlight since the beginning of 2020. Energy is essential to ensure reliable operation and quality care provision in residential aged care communities (RAC). The aim of this study is to determine how RAC's yearly energy use and peak demand changed in Australia and what this might mean for RAC design, operation and energy asset investment and ultimately in the healthcare plan for elderly residents. Five years of electricity demand data from four case study RACs in the same climate zone are analyzed. Statistical tools are used to analyze the data, and a clustering algorithm is used to identify typical demand profiles. A number of energy key performance indicators (KPIs) are evaluated, highlighting their respective benefits and limitations. The results show an average 8% reduction for yearly energy use and 7% reduction for yearly peak demands in the COVID-19 year compared with the average of the previous four years. Typical demand profiles for the four communities were mostly lower in the pandemic year. Despite these results, the KPI analysis shows that, for these four communities, outdoor ambient temperature remains a very significant correlation factor for energy use.

Among various algorithms of multifractal analysis (MFA) for complex networks, the sandbox MFA algorithm behaves with the best computational efficiency. However, the existing sandbox algorithm is still computationally expensive for MFA of large-scale networks with tens of millions of nodes. It is also not clear whether MFA results can be improved by a largely increased size of a theoretical network. To tackle these challenges, a computationally efficient sandbox algorithm (CESA) is presented in this paper for MFA of large-scale networks. Distinct from the existing sandbox algorithm that uses the shortest-path distance matrix to obtain the required information for MFA of networks, our CESA employs the compressed sparse row format of the adjacency matrix and the breadth-first search technique to directly search the neighbor nodes of each layer of center nodes, and then to retrieve the required information. A theoretical analysis reveals that the CESA reduces the time complexity of the existing sandbox algorithm from cubic to quadratic, and also improves the space complexity from quadratic to linear. Then the CESA is demonstrated to be effective, efficient, and feasible through the MFA results of (u,v)-flower model networks from the fifth to the 12th generations. It enables us to study the multifractality of networks of the size of about 11 million nodes with a normal desktop computer.

Recent advances in smart grid technologies have highlighted demand response (DR) as an important tool to alleviate electricity demand-supply mismatches. In this paper, a real-time price (RTP)-based DR algorithm is proposed for industrial facilities, aiming to minimize the electricity cost while satisfying production requirements. In particular, due to future price uncertainties, a data-driven approach is adopted to forecast the future unknown prices for supporting global time horizon optimization, which is realized by long short-term memory recurrent neural network (LSTM RNN). With the aid of predicted prices, the industrial facility energy management is formulated as a mixed integer linear programming (MILP) problem, which is then solved by Gurobi over a rolling horizon basis. Finally, an entire practical steel powder manufacturing process is selected as a case study to verify the RTP-based DR scheme. Numerical simulation results show that the proposed scheme is able to effectively shift energy consumption from peak to off-peak periods and reduce the electricity cost of the facility, while satisfying all of the operating constraints. The performance of the presented data driven RTP forecasting approach is compared to different prediction methods, and error sensitivity analyses are also conducted to evaluate the impact of the RTP uncertainties and the robustness of the proposed RTP-based DR algorithm. Moreover, the DR capability to RTPs is investigated.

Data centres produce waste heat, which can be utilized in district heating systems. However, the mismatch between data centres¿ heat supply and district heating systems¿ heat demands limits its utilization. Further, high peak loads increase the operation cost of district heating systems. This study aimed to solve these problems by introducing thermal energy storages. A water tank and a borehole thermal energy storage system were selected as the short-term and long-term thermal energy storage, respectively. Energy, economic, and environmental indicators were introduced to evaluate different solutions. The case study was a campus district heating system in Norway. Results showed that the water tank could shave the peak load by 31% and save the annual energy cost by 5%. The payback period was lower than 15 years when the storage efficiency remained higher than 80%. However, it had no obvious benefits in terms of mismatch relieving and CO2 emissions reduction. In contrast, the borehole thermal energy storage increased the waste heat utilization rate to 96% and reduced the annual CO2 emissions by 8%. However, the payback period was more than 17 years. These results provide guidelines for the retrofit of district heating systems, where data centres¿ waste heat is available.

Smart grid integrates modern information and communication technologies with the electrical grid for improved efficiency and reliability. In smart grid, the communication infrastructure is composed of wide area networks (WANs), neighborhood area networks (NANs), and home area networks (HANs). There are many real-time applications demanding low-latency communication technologies, such as wide area protection, urgent demand response, and real-time operations. However, the coexistence of various smart grid applications leads to a competition of limited network resources, leading to negative impacts on communication latency, system reliability, etc. To improve the performance of communication latency, multicast and broadcast technologies are efficient approaches, especially for networks with limited bandwidth and large scalability. This chapter offers a systematic introduction to the multicast and broadcast technologies for real-time applications in smart grid, including low-latency multicast to minimize end-to-end delay for wide area control, low-latency multicast for multiple multicast trees with shared links in WANs, and low-latency constrained broadcast in NANs. Aspects of problem formulation, problem-solving, and illustrative examples have been addressed.