A new energy ship is being developed to address energy shortages and greenhouse gas emissions. New energy ships feature low operational costs and zero emissions. This study discusses the characteristics and development of solar-powered ships, wind-powered ships, fuel cell-powered ships, and new energy hybrid ships. Three important technologies are used for the power system of the new energy ship: new-energy spatio-temporal prediction, ship power scheduling, and Digital Twin (DT). Research shows that new energy spatio-temporal prediction reduces the uncertainty for a ship power system. Ship power scheduling technology guarantees safety and low-carbon operation for the ship. DT simulates the navigational environment for the new energy ship to characterize the boundary of the shipboard’s new energy power generation. The future technical direction for new energy ship power systems is also being discussed.

Impedance mismatching can severely decrease the power transmission capacity or even invalid the control logic of wireless power transfer (WPT) systems. To eliminate the impedance mismatch caused by various factors, such as resonant parameter drift, coupling structure misalignment and control strategy, a self-adaptive mistuning correction circuit and corresponding parameter design rules are proposed to ensure the WPT system consistently operates under resonant state, accompanying the optimal power transfer ability. Moreover, a reactive compensation circuit that can create an auxiliary voltage source by the absorbed reactive power that is introduced by impedance mismatching is designed. This source can generate a new current on the coil to bring the mismatched input current back to the resonant state, according to the current superposition principle. Unlike passive impedance network that require high parameter precision and active impedance matching strategies equipped with detection and control circuits, the proposed approach can self-adaptively eliminate either undesired capacitive or inductive reactance with two additional switches and an energy-storage capacitor, improving impedance adjusting rate and providing better system robustness. The simulations and experiments are in good agreement with the theoretical analysis. This article has potential applications in harsh environments where the system impedance and coupling strength are continuously disturbed.

Due to the increasingly serious energy crisis and environmental pollution problem, traditional fossil energy is gradually being replaced by renewable energy in recent years. However, the introduction of renewable energy into power systems will lead to large voltage fluctuations and high capital costs. To solve these problems, an energy storage system (ESS) is employed into a power system to reduce total costs and greenhouse gas emissions. Hence, this paper proposes a two-stage method based on a back-propagation neural network (BPNN) and hybrid multi-objective particle swarm optimization (HMOPSO) to determine the optimal placements and sizes of ESSs in a transmission system. Owing to the uncertainties of renewable energy, a BPNN is utilized to forecast the outputs of the wind power and load demand based on historic data in the city of Madison, USA. Furthermore, power-voltage (P-V) sensitivity analysis is conducted in this paper to improve the converge speed of the proposed algorithm, and continuous wind distribution is discretized by a three-point estimation method. The Institute of Electrical and Electronic Engineers (IEEE) 30-bus system is adopted to perform case studies. The simulation results of each case clearly demonstrate the necessity for optimal storage allocation and the efficiency of the proposed method.

The operating conditions of all-electric tugboats are flexible and changeable. They are more complicated than conventional vessels in terms of joint voyages and power generation scheduling. To guarantee the reliable operation of the ship, a new coordinated optimization scheme that combines economy and operational reliability is proposed. It is based on the various operating conditions of the tugboat during its voyage, taking into account the random outages of equipment and load fluctuations due to speed and wave uncertainties. Due to the difficulty of implementing a stochastic sampling method with space-time coupling constraints (e.g., the voyage is related to propulsion load), an analytical approach is needed to transform the model into a readily solvable mixed-integer linear program (MINP) which attributes risk scenarios to load fluctuations under various conditional probabilities. In addition, this paper proposes an improved piecewise linearization method based on a differential evolutionary algorithm to speed up the solution process and improve computational accuracy. Meanwhile, the energy storage loss cost due to battery degradation is added to the optimization target. The battery’s cycle life is extended by rational scheduling of charging and discharging. Simulations validate this paper’s joint scheduling optimization scheme in multiple comparison experiments. The results show that it can effectively balance the economic and reliability levels under various risk scenarios and improve the environmental energy efficiency indicators.