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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. [ABSTRACT FROM AUTHOR]

As a result of the increasing penetration of renewables in power systems, wind farms (WFs) have to satisfy the low-voltage ride-through (LVRT) requirement to stay connected to the power grid under fault conditions. This study proposes a novel method that uses the static synchronous compensator (STATCOM) to improve the LVRT capability of WFs during grid faults. The proposed method is based on the Taguchi method which involves orthogonal experiments. The allocation (locations and sizes) and fuzzy control gains of STATCOM are determined by the analysis of mean (ANOM) technique as part of the Taguchi method to achieve a robust design that is insensitive to variations of operating conditions and faults. The optimal STATCOM that is obtained by the proposed method is validated in both a single WF-infinite bus power system and the IEEE 39-bus power system. The simulation results show that the proposed method can enhance the LVRT capability of WFs. [ABSTRACT FROM AUTHOR]

• The convolution and pooling operations are used to extract the wind power feature. • A double Gaussian activation function is proposed for Radial Basis Function network. • Coefficients of determination (r-square) of the results exceed 0.88 in 4 seasons. Wind power generation is always associated with uncertainties as a result of fluctuations of wind speed. Accurate predictions of wind power generation are important for the efficient operation of power systems. This paper presents a hybrid deep learning neural network for 24 h-ahead wind power generation forecasting. This novel method is based on a Convolutional Neural Network (CNN) that is cascaded with a Radial Basis Function Neural Network (RBFNN) with a double Gaussian function (DGF) as its activation function. The CNN is utilized to extract wind power characteristics by convolution, kernel and pooling operations. The supervised RBFNN, incorporating a DGF, deals with uncertain characteristics. Realistic wind power generations, measured on a wind farm, were used in simulations. The proposed method is implemented using TensorFlow and Keras Library. Comparative studies of different approaches are shown. Simulation results reveal that the proposed method is more accurate than traditional methods for 24 h-ahead wind power forecasting. [ABSTRACT FROM AUTHOR]

Due to the global concern on the increasing amount of fossil energy consumed by traditional ships, the application of renewable energy into a ship power system provides a new solution to improve the energy efficiency and to reduce the greenhouse gas emissions. This study proposes a stand-alone power system on a large oil tanker including wind generation system, photovoltaic generation system, the diesel generator, and the energy storage system (ESS). Unlike on land, the wind generation on the shipboard not only relies on the natural wind speed but also the ship's course and speed. The installation of the wind turbines on the board is optimally designed, which takes the relative speed into account. In order to mitigate the intermittence of the renewable energy generation, a lead–acid battery serves as the ESS to enhance the stability of the ship power system, and the size is optimized by the multi-objective particle swarm optimization to minimize the whole system cost and CO2emissions. Additionally, variations of the ship loads are considered with respect to the different operational conditions. Various cases are compared in detail to demonstrate the applicability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

For the operators of a power system, underfrequency load shedding is important for maintaining the system in a stable and reliable state whenever it suffers from a severe disturbance. The underfrequency relay protects the power system against a blackout when the system frequency declines to predetermined settings. This paper determines the shed load at each stage for the underfrequency relay (81L). The genetic algorithm (GA) is employed to minimize the shed load and maximize the lowest swing frequency. Penalty functions and chromosomes with varying lengths are utilized in the GA to determine the optimal shed loads at all stages. The uncertainty in bus loads is modeled by a fuzzy set. A stand-alone power system with diesel and wind-power generators is adopted to demonstrate the applicability of the proposed method. [ABSTRACT FROM PUBLISHER]

Abstract: Wind energy is currently one of the types of renewable energy with a large generation capacity. However, since the operation of wind power generation is challenging due to its intermittent characteristics, forecasting wind power generation efficiently is essential for economic operation. This paper proposes a new method of wind power and speed forecasting using a multi-layer feed-forward neural network (MFNN) to develop forecasting in time-scales that can vary from a few minutes to an hour. Inputs for the MFNN are modeled by fuzzy numbers because the measurement facilities provide maximum, average and minimum values. Then simultaneous perturbation stochastic approximation (SPSA) algorithm is employed to train the MFNN. Real wind power generation and wind speed data measured at a wind farm are used for simulation. Comparative studies between the proposed method and traditional methods are shown. [ABSTRACT FROM AUTHOR]

Under-frequency load shedding (UFLS) prevents a power grid from a blackout when a severe contingency occurs. UFLS schemes can be classified into two categories—event-based and response-driven. A response-driven scheme utilizes 81L relays with pre-determined settings while an event-based scheme develops a pre-specified look-up table. In this work, an event-based UFLS scheme is presented for use in an offshore standalone power grid with renewables to avoid cascading outages due to low frequency protection of wind power generators and photovoltaic arrays. Possible "N-1" and "N-2" forced outages for peak and off-peak load scenarios in summer and winter are investigated. For each forced outage event, the total shed load is minimized and the frequency nadir is maximized using particle swarm optimization (PSO). In order to reduce the computation time, initialization and parallel computing are implemented using MATLAB/Simulink because all forced outage events and all particles in PSO are mutually independent. A standalone 38-bus power grid with two wind turbines of 2 × 2 MW and photovoltaics of 7.563 MW was studied. For each event, the proposed method generally obtains a result with a smaller shed load and a smaller overshoot frequency than the utility and existing methods. These simulation results verify that the proposed method is practically applicable in a standalone power system with penetration of renewables. [ABSTRACT FROM AUTHOR]