Search

As a large number of flexible elements such as distributed power and flexible load are connected to microgrids, the economic improvement of microgrids has become an important topic through rational energy distribution. In this paper, an economic scheduling model considering the load demand for a microgrid system under the mechanism of a peak–valley tariff is proposed. A mathematical model of the microgrid components is proposed to determine the exchange power between the microgrid and main network. Meanwhile, an improved War Strategy Optimization (WSO) algorithm is proposed to investigate three scenarios: (i) without batteries, (ii) with batteries and (iii) with batteries and demand response. Additionally, demand response optimization is carried out with the Particle Swarm Optimization (PSO) algorithm and the improved WSO is compared with four other algorithms in scenario (iii). The comparison shows that the improved WSO algorithm has a better optimization performance in solving the proposed scheduling model. [ABSTRACT FROM AUTHOR]

To meet the increasing demand for clean energy storage in modern society, the development of efficient and low-cost electrocatalysts that can overcome and accelerate the sluggish kinetics of electrochemical reactions is required. NiFe-Layered Double Hydroxide (NiFe-LDH) is regarded as an effective oxygen evolution reaction (OER) electrocatalyst, but most of the current synthesis methods, such as electrochemical deposition and calcination, are complex and difficult to operate on a large scale. Herein, we report the preparation of NiFe-LDH directly on a NiFe foam substrate using a simple two-step method in which the surface oxide layer is first removed from NiFe foam using a room-temperature hydrochloric acid bath for 10 min, followed by soaking in hydrochloric acid solution at 80 °C for 20 h. The prepared NiFe foam etched by hydrochloric acid for 20 h (NiFe-20-H) exhibited a unique hydrangea flower-like structure with a large surface area and abundant active sites, which is favorable for OER. Combining the structural advantages of large number of exposed active sites, synergistic effects of nickel and iron, and the convenient charge transfer path provided by the NiFe foam, the resulting NiFe-20-H sample achieved a current density of 10 mA cm−2 at an extremely low overpotential (241 mV) and a small Tafel slope of 44.2 mV dec−1, providing excellent long-term stability in alkaline electrolyte, surpassing pristine NiFe foam reported in our work, as well as many state-of-the-art electrocatalysts and IrO 2. This efficient synthesis of NiFe-LDH provides a new approach for the development of non-noble OER electrocatalysts and has wide application prospects in the field of electrocatalysts. [Display omitted] • NiFe-LDH with flower-like structure was designed by a simple two-step hydrochloric acid treatment. • The existence of NiFe-LDH was proved by a series of qualitative and quantitative characterization methods. • NiFe-20-H has excellent electrocatalytic performance for oxygen evolution reaction. • The reason for the outstanding OER performance of NiFe-20-H is discussed. [ABSTRACT FROM AUTHOR]

With the rise of edge computing technology and the development of intelligent mobile devices, task offloading in the edge-cloud environment has become a research hotspot. Task offloading is also a key research issue in Mobile CrowdSourcing (MCS), where crowd workers collect sensed data through smart devices they carry and offload to edge-cloud servers or perform computing tasks locally. Current researches mainly focus on reducing resource consumption in edge-cloud servers, but fails to consider the conflict between resource consumption and service quality. Therefore, this paper considers the learning generation offloading strategy among multiple Deep Neural Network(DNN), proposed a Deep Neural Network-based Task Offloading Optimization (DTOO) algorithm to obtain an approximate optimal task offloading strategy in the edge-cloud servers to solve the conflict between resource consumption and service quality. In addition, a stack-based offloading strategy is researched. The resource sorting method allocates computing resources reasonably, thereby reducing the probability of task failure. Compared with the existing algorithms, the DTOO algorithm could balance the conflict between resource consumption and service quality in traditional edge-cloud applications on the premise of ensuring a higher task completion rate. [ABSTRACT FROM AUTHOR]