With the development of techniques, such as the Internet of Things (IoT) and edge computing, home energy management systems (HEMS) have been widely implemented to improve the electric energy efficiency of customers. In order to automatically optimize electric appliances’ operation schedules, this paper considers how to quantitatively evaluate a customer’s comfort satisfaction in energy-saving programs, and how to formulate the optimal energy-saving model based on this satisfaction evaluation. First, the paper categorizes the utility functions of current electric appliances into two types; time-sensitive utilities and temperature-sensitive utilities, which cover nearly all kinds of electric appliances in HEMS. Furthermore, considering the bounded rationality of customers, a novel concept called the energy-saving cost is defined by incorporating prospect theory in behavioral economics into general utility functions. The proposed energy-saving cost depicts the comfort loss risk for customers when their HEMS schedules the operation status of appliances, which is able to be set by residents as a coefficient in the automatic energy-saving program. An optimization model is formulated based on minimizing energy consumption. Because the energy-saving cost has already been evaluated in the context of the satisfaction of customers, the formulation of the optimization program is very simple and has high computational efficiency. The case study included in this paper is first performed on a general simulation system. Then, a case study is set up based on real field tests from a pilot project in Guangdong province, China, in which air-conditioners, lighting, and some other popular electric appliances were included. The total energy-saving rate reached 65.5% after the proposed energy-saving program was deployed in our project. The benchmark test shows our optimal strategy is able to considerably save electrical energy for residents while ensuring customers’ comfort satisfaction is maintained.

With the increasing complexity of power systems and the proliferation of renewable energy sources, the task of calculating carbon emissions has become increasingly challenging. To address these challenges, we developed a new method for predicting carbon emission factors. Bayesian optimization technique graphical convolutional networks with long- and short-term network (BO-TGNN) is used to predict the carbon emissions of the power system. The method aims to quickly predict the day-ahead carbon emissions of power system nodes with enhanced feature extraction and optimized network training hyperparameters. The effectiveness of the proposed method is demonstrated through simulation tests on three different power systems using four deep learning algorithms. The method provides a tailored solution to the evolving needs of carbon reduction efforts and is a significant step forward in addressing the complexity of carbon emission calculations for modern power systems.

Carbon emission estimation for power systems is essential for identifying emission responsibilities and formulating effective mitigation measures. Current carbon emission prediction methods for power systems exhibit limited computational efficiency and inadequate noise immunity under complex operating conditions. In this study, we address these limitations by improving population initialization, search mechanisms, and iteration strategies and developing a hybrid strategy Modified Dung Beetle Optimization (MDBO) algorithm. This led to the development of an MDBO-enhanced Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) network hybrid prediction model for carbon emission prediction. Firstly, the theoretical calculation mechanism of carbon emission flow in power systems is analyzed. Subsequently, an MDBO-CNN-LSTM deep network architecture is constructed, with detailed explanations of its fundamental structure and operational principles. Then, the proposed MDBO-CNN-LSTM model is utilized to predict the nodal carbon emission factor of power systems with the integration of renewable energy sources. Comparative experiments with conventional CNN-LSTM models are conducted on modified IEEE 30-, 118-, and 300-bus test systems. The results show that the maximum mean squared error of the proposed method does not exceed 0.5734% in the strong-noise scenario for the 300-bus system, which is reduced by half compared with the traditional method. The proposed method exhibits enhanced robustness under strong noise interference, providing a novel technical approach for precise carbon accounting in power systems.

As global energy demand rises and climate change poses an increasing threat, the development of sustainable, low-carbon energy solutions has become imperative. This study focuses on optimizing shared energy storage (SES) and distribution networks (DNs) using deep reinforcement learning (DRL) techniques to enhance operation and decision-making capability. An innovative dynamic carbon intensity calculation method is proposed, which more accurately calculates indirect carbon emissions of the power system through network topology in both spatial and temporal dimensions, thereby refining carbon responsibility allocation on the user side. Additionally, we integrate user-side SES and ladder-type carbon emission pricing into DN to create a low-carbon economic dispatch model. By framing the problem as a Markov decision process (MDP), we employ the DRL, specifically the deep deterministic policy gradient (DDPG) algorithm, enhanced with prioritized experience replay (PER) and orthogonal regularization (OR), to achieve both economic efficiency and environmental sustainability. The simulation results indicate that this method significantly reduces the operating costs and carbon emissions of DN. This study offers an innovative perspective on the synergistic optimization of SES with DN and provides a practical methodology for low-carbon economic dispatch in power systems.

Josephson voltage standards (JVSs), which are based on fundamental physical constants, serve as natural voltage references with exceptional accuracy and long-term stability. They play a foundational role in modern precision metrology and in the development of advanced scientific instruments and measurement technologies. This paper reviews their fundamental principles, historical development, key technologies, and application progress. First, the physical mechanism of voltage reproduction based on the Josephson effect is explained. Building on this basis, the working principles, technical features, and research status of three types of standards, i.e., the conventional Josephson voltage standard (CJVS), the programmable Josephson voltage standard (PJVS), and the Josephson arbitrary waveform synthesizer (JAWS), are described in detail. Second, a series of international key comparisons conducted to validate consistency among different Josephson voltage standards is summarized. Next, novel voltage measurement techniques based on Josephson voltage standards are described, with a focus on their broad applications in establishing quantum-based power standards, enabling precise measurements with Kibble/Joule balances, developing noise thermometers, and calibrating advanced metrology standards. Furthermore, directions for further exploration in quantum measurement techniques based on Josephson quantum voltage standards are discussed, and future prospects are outlined. Finally, conclusions are presented.

The low-carbon transition of the power sector is fundamental to achieving “Dual Carbon” goals, where demand-side management plays an increasingly vital role in transforming flexible loads into renewable energy accommodation and active emission-reduction resources. However, existing low-carbon demand response mechanisms based on dynamic carbon emission factors only reflect average system states and fail to quantify the incremental carbon impact of marginal load changes. To address this limitation, this paper proposes a novel marginal carbon emission factor-driven low carbon demand response mechanism. Unlike traditional methods, the proposed mechanism utilizes marginal carbon emission factors as a high-sensitivity guiding signal to inform users of the real-time emission and renewable energy consumption variations caused by their consumption adjustments. Furthermore, considering the forecasting errors of high-penetration renewable energy, the uncertainty of marginal carbon emission factors is explicitly considered. Case studies are conducted to compare the proposed method with the conventional method through comparative analyses based on the modified PJM-5 system. Results demonstrate that the MCEF-driven approach provides more precise carbon-reduction and renewable energy utilization signals to achieve superior system-wide decarbonization performance and sustainable development.