In order to reach carbon neutrality, there is growing interest in reducing greenhouse gas (GHG) and improving energy efficiency. One way to address this issue is the optimal scheduling of the integrated energy system (IES) with multiple combined cooling heating and power (CCHP) systems as proposed in this article. We model IES as a device with multiple input/output ports by the energy hub (EH) framework and propose a multiobjective optimal model to improve energy efficiency and reduce GHG emissions. The proposed model is constructed as a mixed-integer nonlinear programming (MINLP) due to considering nonlinear couplings of multiple energy flows and the unit commitment of multiple CCHP systems. To improve the computational efficiency, the proposed MINLP model is transformed into a nonlinear programming (NLP) model by a fast unit commitment technique based on the approximation of the aggregated online capacity. Finally, simulation results show the effectiveness of the proposed approach in reducing GHG emissions and improving energy efficiency as well as computational efficiency.

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.

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.

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.

The design of an effective and reliable communication network supporting smart grid applications requires the selection of appropriate communication technologies and protocols. The objective of this study is to study and quantify the capabilities of an advanced metring infrastructure (AMI) to support the simultaneous operation of major smart grid functions. These include smart metring, price‐induced controls, distribution automation, demand response, and electric vehicle charging/discharging applications in terms of throughput and latency. OPNET is used to simulate the performance of selected communication technologies and protocols. Research findings indicate that smart grid applications can operate simultaneously by piggybacking on an existing AMI infrastructure and still achieve their latency requirements.

Due to the depletion of energy resources and increased energy demand, there is an increased focus on the energy consumption and management in buildings. Many building energy management (BEM) software platforms are commercially available to monitor and control energy consumption. These platforms are hosted on the physical hardware within the building, due to which the hardware specifications limit their performance. To address this limitation, cloud technology emerged which facilitates software to be deployed at a remote location that has scalable hardware resources. Conventional BEM software can leverage such a cloud platform to offer scalable and maintenance-free installation. Once hosted on a remote cloud platform, BEM software lacks direct connectivity to building sensors/controllers, hence requires a device to support remote accessibility. Most devices are bound to a local area network and therefore, need an additional functional layer on top of its communication stack to perform network address translation (NAT)-Traversal. This functionality is implemented on a scalable software which connects to the devices in the network and acts as a gateway for cloud-based BEM software to access devices in the local area network. Any message sent to this gateway is translated to a respective device protocol. This paper describes the design and implementation of such an Internet of Things (IoT) gateway for a cloud-based BEM system that requires support for BACnet, Modbus, and HTTP RESTful interface devices.

The analysis of large scale data logged from complex cyber-physical systems, such as microgrids, often entails the discovery of invariants capturing functional as well as operational relationships underlying such large systems. We describe a latent factor approach to infer invariants underlying system variables and how we can leverage these relationships to monitor a cyber-physical system. In particular we illustrate how this approach helps rapidly identify outliers during system operation.

Provides an overview of the technical articles and features presented in this issue.

This paper describes the application of a phase-space embedding concept to artificial neural network (ANN) based short-term electric load forecasting. Embedding parameters for electric load time-series were determined using the method of integral local deformation. In the reconstructed phase-space modular ANN predictor was trained to predict loads up to five days ahead in one-hour steps. It was found that addition of temperature and cycle variables to the phase-space based input variable set improved forecasting accuracy. The overall number of input variables was much smaller than in the similar cases reported in the literature. In this manner the size of historical data set needed for training was significantly reduced. Forecasting errors were comparable to or better than the ones reported for the similar cases Such characteristics make the approach attractive for short-term load forecasting in the deregulated electric power industry.

“America’s Energy Supply” was written by C. P. Steinmetz (Fig. 1), who was president of the American Institute of Electrical Engineers (AIEE) in 1901 and a partner with Edison in the early days of the General Electric Company. This paper addresses America’s energy supply from the beginning of the twentieth century. There are three themes in this paper: efficiency and effectiveness of coal and hydrogeneration, cogeneration (to increase efficiency), and solar energy. In the 100-year period between the early 1800’s and early 1900’s, there was a steady increase in coal production from year to year, which experienced almost no interruptions from most of the catastrophic political and industrial convulsions of the nineteenth century, namely, the Civil War and the industrial panic of the 1890’s. Almost 80 years ago, when this paper was written, the use of coal was increasing at a rate of 6‐10% per year, which led the author to predict that at that rate, 10 000 million tons of coal would be produced in the United States in 1958. But alternate sources of energy were quickly gaining acceptance after World War II. Even though the prediction did not materialize, primarily because of the advent of natural gas and oil as preferred energy sources, coal production has indeed steadily increased since then, reaching just over 1 billion tons in 1996, one-tenth of Steinmetz’s prediction for 1958. As coal and hydropower were used to supply the country’s electric power needs at the time, one obvious question was what fuel source could be used to keep up with the growing demand for electricity. Hydropower, at its fullest potential of 230 million kW-h, could supply the total energy needs of the early twentieth century. That meant that as the energy demands grew, and the share of coal in the “generation mix” began to fall, the reliance on hydropower could not be total. This was probably the strongest argument for efforts to increase the efficiency of the methods for using coal. In the early 1900’s, the average efficiency of coal

The formulation of rules for the rule base and the application of such rules are discussed. The classification of the load forecast parameters into weather-sensitive and nonweather-sensitive categories is described. The rationale underlying the development of rules for both the one-day and seven-day forecast is presented. This exercise leads to the identification and estimation of parameters relating load, weather variables, day types, and seasons. Sample rules that are the product of identifiable statistical relationships and expert knowledge are examined. A self-learning process is described which shows how rules governing the electric utility load can be updated. Results from both the one-day and seven-day forecast algorithms are presented, where the seven-day forecast is generated using both accurate and predicted weather information. The monthly average load forecast errors range between 2.97% and 10.71% for the seven-day forecasts. For the one-day forecasts, the average seasonal errors range between 1.03% and 1.53%.