An interconnected multi-microgrids (IMMGs) system takes advantage of various complementary power sources and effectively coordinates the energy sharing/trading among the MGs and the main grid to improve the stability, reliability, and energy efficiency of the system. The core of this structure is to achieve the optimal distribution of energy sharing through proper strategies. However, the volatility and intermittent characteristics of renewable resources, time-varying loads in the MGs, their correlated power generations, and the coupled energy among the MGs during energy trading, all bring about new challenges to achieving a stable operation and optimal scheduling in the power system. Many solutions have been proposed to solve these problems. In this paper, we provide an overview of the current energy management systems (EMS) in IMMGs, focusing on the IMMG structure, EMS objectives, timescales, and scheduling optimization structure. We then provide a review of the distributed optimization algorithms in IMMGs. We conclude this survey with a discussion of future directions. INDEX TERMS Smart grid, microgrid (MG), interconnected multi-microgrids (IMMG), energy sharing, energy management.

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As an emerging mode of transport, bike-sharing is being quickly accepted by Chinese residents due to its convenience and environmental friendliness. As hotspots for bike-sharing, railway-station service areas attract thousands of bikes during peak hours, which can block roads and pedestrian walkways. Of the many works devoted to the connection between bikes and rail, few have addressed the spatial‒temporal pattern of bike-sharing accumulating around station service areas. In this work, we investigate the distribution patterns of bike-sharing in station service areas, which are influenced not only by railway-station ridership but also by the built environment around the station, illustrating obvious spatial heterogeneity. To this end, we established a geographic weighted regression (GWR) model to capture this feature considering the variables of passenger flow and the built environment. Using the data from bike-sharing in Beijing, China, we applied the GWR model to carry out a spatiotemporal characteristic analysis of the relationship between bike-sharing usage in railway-station service areas and its determinants, including the passenger flow in stations, land use, bus lines, and road-network characteristics. The influence of these factors on bike-sharing usage is quite different in time and space. For instance, bus lines are a competing mode of transport with bike-sharing in suburban areas but not in city centers, whereas industrial and residential areas could also heavily affect the bike-sharing demand as well as railway-station ridership. The results of this work can help facilitate the dynamic allocation of bike-sharing and increase the efficiency of this emerging mode of transport.

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The accuracy of traditional flatness control methods are limited and it is difficult to establish a precise mathematical model of the rolling mill. In addition, the flatness control system is complex and multivariate. General model approaches can not satisfy the high precision demand of rolling process. In this paper, T-S cloud inference neural network and its stability are proposed. It is constructed by cloud model and T-S fuzzy neural network. The stability of T-S cloud inference neural network is analyzed by Lyapunov method in details. Based on the new network, flatness recognition model and flatness predictive model are established. And they are applied for 900HC reversible cold rolling mill. The flatness control system is designed and a simple controller is developed. Initial parameters of the controller are firstly determined through offline training based on measured data, and then they are optimized online automatically. Genetic Algorithm (GA) is used as the optimizing method which is compared with particle swarm optimization (PSO). The simulation results demonstrate that the flatness control system is effective and has a better precision and robustness.

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A single-objective optimization energy management strategy (EMS) for an onboard hybrid energy storage system (HESS) for light rail (LR) vehicles is proposed. The HESS uses batteries and supercapacitors (SCs). The main objective of the proposed optimization is to reduce the battery and SC losses while maintaining the SC state of charge (SOC) within specific limits based on the distance between consecutive LR stations. To do this, a series of optimized SOC limits is used to prevent the SC from becoming exhausted prematurely instead of the standard SC SOC penalty term in the cost function. Meanwhile, a rule-based EMS (RB-EMS) is used to give the SCs charging priority over the batteries when the vehicle is braking. Moreover, a simplified method for the optimization is proposed to reduce the computational burden. Simulation and experimental results for the proposed EMS and a standard SC SOC penalty-based cost function optimization are provided to evaluate losses. As a result, it is shown that the proposed EMS, compared with standard SC SOC penalty-based cost function optimization, decreases losses and prevents the SOC from reach the discharging limits.