This study proposes a model predictive control (MPC)‐based home energy management system for residential microgrid (RM) in which all related information such as the time‐varying information of the load demand, electricity price and renewable energy generations, are all taken into account. A novel finite‐horizon mixed‐integer linear programming problem is iteratively formulated to investigate the optimal control actions of the RM under an MPC framework. Three case studies are conducted to discuss the technical and economic impacts of the responsive electrical and thermal loads, plug‐in hybrid electric vehicles, and electrical and thermal energy storage units. Moreover, a sensitivity analysis is performed to demonstrate the superiority of the proposed approach when forecasts of related information are imperfect.

In this paper, the synchronizing dynamic issue of VSC dominated power system is focused on. First, based on the discovered analogous synchronizing dynamical process of VSC (voltage source converter) and SG (synchronous generator), the dynamics of VSC's PLL (phase locked loop) is modeled in the similar form of SG's rotor motion. The influence of control interaction on PLL is modeled in the same way as that of control dynamics on rotor motion. Then the analogized SG model of PLL-based VSC is developed. Based on the proposed model, the analysis idea jointing accelerating-decelerating area method and numerical simulation is employed to study the transient synchronizing stability of single VSC infinite bus system. It is identified that the influence of ACC (alternating current control) on PLL is to accelerate PLL's equivalent motion in first swing, which aggravates the mismatch of accelerating-decelerating area and thus deteriorates the transient stability. Furtherly, the influence factors including ACC's bandwidth, PLL's bandwidth and current references on the transient stability are studied. This paper provides a useful analysis idea for transient synchronizing stability analysis of VSC with influence of control interaction considered.

Due to the increasing concerns about the environmental and economic issues of traditional ships, all-electric ships with energy storage and renewable energy integration have become more and more appealing for the forthcoming future. In this paper, an optimal energy storage system (ESS) capacity determination method for a marine ferry ship is proposed; this ship has diesel generators and PV panels. ESSs sizing optimization and power system scheduling optimization are simultaneously conducted and it is converted to a mixed-integer quadratic programming (MIQP) model with special modeling techniques. The case study shows that the proposed method is flexible and effective, and the relationships between the ESSs size and the discharge rate, life cycle times, or initial investment cost are investigated.

In the context of harsh emission control and ecological environment protection, the shipping industry is transforming and upgrading towards greening, decarburization, and electrification. Battery-powered all-electric inland ships have been attracting increasingly attention. However, its initial investment cost is much more expensive than a traditional diesel-driven mechanical ship because lithium-ion batteries are currently expensive. Hence, a suitable battery size and efficient energy management strategy for ship sailing are very important for a battery-powered ship. In this paper, a novel joint optimization method of the sailing speed and battery capacity, which considers the interaction between battery size and sailing speed as well as multiple operation factors, such as freight demand and battery life, and port electricity price, is proposed to fully exploit the battery-powered ships’ application potential. Moreover, a joint optimization model of the sailing speed and battery energy consumption model considers the battery-powered ship’s characteristics and waterway characteristics. Next, a solution algorithm for the proposed joint optimization model is established to achieve joint decision-making regarding the sailing speed and battery size. Finally, case studies are conducted to demonstrate the flexibility and effectiveness of the proposed method. The results show that the proposed method can obtain the optimal sailing speed and the corresponding battery capacity synchronously when the actual transportation scenario is fixed. Moreover, the battery initial investment cost can be effectively reduced with the prosed method.