The ability to jump is found widely among small animals such as frogs, grasshoppers, and fleas. They jump to overcome large obstacles relative to their small sizes. Inspired by the animals' jumping capability, a miniature jumping robot-Michigan State University (MSU) Jumper-has been developed. In this paper, the mechanical design, fabrication, and experimentation of the MSU jumper are presented. The robot can achieve the following three performances simultaneously, which distinguish it from the other existing jumping robots. First, it can perform continuous steerable jumping that is based on the self-righting and the steering capabilities. Second, the robot only requires a single actuator to perform all the functions. Third, the robot has a light weight (23.5 g) to reduce the damage that results from the impact of landing. Experimental results show that, with a 75° take-off angle, the robot can jump up to 87 cm in vertical height and 90 cm in horizontal distance. The robot has a wide range of applications such as sensor/communication networks, search and rescue, surveillance, and environmental monitoring.

The plug-in electric vehicle (PEV) has attracted more and more attention because of the energy crisis and environmental pollution, which is also the main shiftable load of the residential users’ demand side management (DSM) system in the future smart grid (SG). In this paper, we employ game theory to provide an autonomous energy management system among residential users considering selling energy back to the utility company by discharging the PEV’s battery. By assuming all users are equipped with smart meters to execute automatic energy consumption scheduling (ECS) and the energy company can adopt adequate pricing tariffs relating to time and level of energy usage, we formulate an energy management game, where the players are the residential users and the strategies are their daily schedules of household appliance use. We will show that the Nash equilibrium of the formulated energy management game can guarantee the global optimization in terms of minimizing the energy costs, where the depreciation cost of PEV’s battery because of discharging and selling energy back is also considered. Simulation results verify that the proposed game-theoretic approach can reduce the total energy cost and individual daily electricity payment. Moreover, since plug-in electric bicycles (PEBs) are currently widely used in China, simulation results of residential users owing household appliances and bidirectional energy trading of PEBs are also provided and discussed.

Multi‐microgrid is an integrated system of microgrids, distributed generations, and battery energy storage system (BESS). As the significant equipment in microgrid, BESS can perform multitasking, such as load management and peak shaving. This study mainly focuses on the energy consumption scheduling of multi‐microgrid considering the optimisation of BESS capacity. Energy management with BESS optimisation is studied by considering the cost of distributed generations, cost of BESS, and bidirectional energy trading. The optimisation problem is tackled from two different aspects: an individual‐oriented optimisation and a coalition‐based optimisation. In the first approach, each microgrid is optimised individually with a non‐cooperative game; while in the second approach, the joint optimisation of all microgrids is formulated through cooperation among multi‐microgrids. In order to achieve the optimal energy consumption strategy and BESS capacity, distributed algorithms for two formulations are presented, which combine particle swarm optimisation and interior point method. Simulation results show that both approaches can contribute to peak shaving and reducing the daily cost of multi‐microgrid.

Household plug-in electric vehicles (PEVs) are attracting much attention in demand-side management (DSM) for shifting loads from peak hours. This paper proposes a scenario for DSM programs to schedule household energy consumption considering bidirectional energy trading of PEVs. Residential community is deployed as an agent of native users in the scenario, and also the charging cost and discharging profit of PEVs are not public information among different communities. To optimize energy consumption scheduling among communities with incomplete information, a Bayesian game approach is formulated. The existence of Bayesian Nash equilibrium is proved mathematically. To implement the proposed Bayesian game approach, an effective iterative algorithm is presented. Simulation results show that the proposed Bayesian game approach can benefit all participated users, reduce the peak-to-average ratio of the overall energy demand.

The forecasting of the load profile of the domestic sector is an area of increased concern for the power grid as it appears in many applications, such as grid operations, demand side management, energy trading, and so forth. Accordingly, a bottom-up forecasting framework is presented in this paper based upon bottom level data about the electricity consumption of household appliances. In the proposed framework, a load profile for group households is obtained with a similar day extraction module, household behavior analysis module, and household behavior prediction module. Concretely, similar day extraction module is the core of the prediction and is employed to extract similar historical days by considering the external environmental and household internal influence factors on energy consumption. The household behavior analysis module is used to analyse and formulate the consumption behavior probability of appliances according to the statistical characteristics of appliances’ switch state in historical similar days. Based on the former two modules, household behavior prediction module is responsible for the load profile of group households. Finally, a case study based on the measured data in a practical residential community is performed to illustrate the feasibility and effectiveness of the proposed bottom-up household load forecasting approach.