The advancements in tactile sensor technology have found wide-ranging applications in robotic fields, resulting in remarkable achievements in object manipulation and overall human-machine interactions. However, the widespread availability of high-resolution tactile skins remains limited, due to the challenges of incorporating large-sized, robust sensing units and increased wiring complexity. One approach to achieve high-resolution and robust tactile skins is to integrate a limited number of sensor units (taxels) into a flexible surface material and leverage signal processing techniques to achieve super-resolution sensing. Here, we present a magnetic skin consisting of multi-direction magnetized flexible films and a contactless Hall sensor array. The key features of the proposed sensor include the specific magnetization arrangement, K-Nearest Neighbors (KNN) clustering algorithm and convolutional neural network (CNN) model for signal processing. Using only an array of 4*4 taxels, our magnetic skin is capable of achieving super-resolution perception over an area of 48400 mm², with an average localization error of 1.2 mm. By employing neural network algorithms to decouple the multi-dimensional signals, the skin can achieve multi-point and multi-scale perception. We also demonstrate the promising potentials of the proposed sensor in intelligent control, by simultaneously controlling two vehicles with trajectory mapping on the magnetic skin.

Green hydrogen, produced from renewable energy sources, is undergoing a global resurgence as a clean energy solution driving decarbonization and fueling sustainable mobility. However, the major challenge facing the hydrogen economy is its scalability, particularly in the transportation sector. In response, this research develops innovative business models that utilize the low-emission attributes of green hydrogen to alleviate its financial constraints. The decrease in costs, in turn, can stimulate the growth of green hydrogen-powered transportation and facilitate the transition toward a carbon-neutral future. Key components of the proposed business workflow are elaborated, mathematical formulations of market parameters are derived, and case studies are presented to demonstrate the feasibility and efficiency of the proposed business models. Notably, the results demonstrate that the substantial costs associated with current hydrogen-powered transportation can be effectively subsidized in the proposed business models. Under current carbon emission prices of 92 USD/ton, the levelized cost of driving can be halved. Furthermore, these models create new opportunities for stakeholders across the entire hydrogen-powered transportation value chain, contributing to the overall prosperity and growth of the hydrogen industry. This highlights the significance and promising potential of the proposed business models within the green hydrogen credit framework.

With the target of carbon peaking and carbon neutrality, renewable energy generation (REG) develops rapidly. The increasing penetration of REG brings along the problems of fluctuation in power flow and the possible abandonment of wind and photovoltaics (PV) generation. In this context, the so-called integrated energy system (IES) becomes a promising solution to the accommodation of REG thanks to energy storage systems and coupling devices inside. In this paper, the optimal operation model of an IES is first presented, with the schemes of green certificate trading and carbon emission right trading included to provide economic incentives for accommodating REG. Next, in order to address the problem of uncertainty in REG, the devices in the IES are divided into three types based on regulation flexibility, and a multi-time period optimal dispatching scheme is proposed, including day-ahead optimal scheduling, rolling optimal dispatching, and real-time control strategy. Finally, it is demonstrated by simulation results of a numerical example that the proposed method not only promotes the accommodation capability for REG but can also cope well with contingencies.

Governments around the world have been developing intermittent renewable energy generation (IREG) technologies such as wind power and solar power aiming at securing energy supply and carbon emission reduction. However, the uncertainty of intermittent renewable energy-based generation brings challenges to a power system with a high penetration of IREG. Given this background, through joint energy and frequency regulation service (FRS) clearing, a new two-sided spot electricity market mechanism is proposed in this paper, which can quantitatively determine the FRS demand of the system to cope with the high IREG penetration rate. The proposed mechanism also incorporates the consumer side to manage uncertainties of both IREG outputs and flexible loads, via providing economic incentives to market participants. Besides, in the proposed market mechanism, carbon emission is included in the market clearing function, which can not only contribute to the decarbonization of electricity resources, but also promote the coupling relationship between electricity and carbon emission markets. Subsequently, the correlation analysis between electricity and carbon prices confirms the positive effect of the proposed mechanism on deepening the electricity-carbon synergy. Finally, case studies demonstrate the feasibility and effectiveness of the proposed market mechanism.

The well-known dispatcher training simulator (DTS), as a good tool to train power system dispatchers, has been widely used for over 40 years. However, with the high-speed development of the smart grid, the traditional DTSs have struggled to meet the power industry’s expectations. To enhance the effectiveness of dispatcher training, technical innovations in DTSs are becoming more and more demanding. Meanwhile, the ever-advancing artificial intelligence (AI) technology provides the basis for the design of intelligent DTSs. This paper systematically reviews the traditional DTS in terms of its origin, structure, and functions, as well as limitations in the context of the smart grid. Then, this paper summarizes the AI techniques commonly used in the field of power systems, such as expert systems, artificial neural networks, and the fuzzy set theory, and employs them to develop intelligent DTSs. Regarding a less studied aspect of DTSs, i.e., intelligent training control, we introduce the Adaptive Learning System (ALS) to develop a personalized training program, which will also be an important aspect of future research.