Due the concern about serious environmental pollution and fossil energy consumption, introducing solar generation into ship power systems has drawn greater attention. However, the penetration of solar energy will result in ship power system instability caused by the uncertainties of the solar irradiation. Unlike on land, the power generated by photovoltaic (PV) modules on the shipboard changes as the ship rolls. In this paper, a high-speed flywheel energy storage system (FESS) is modeled to smooth the PV power fluctuations and improve the power quality on a large oil tanker which contains a PV generation system, a diesel generator, a FESS, and various types of ship loads. Furthermore, constant torque angle control method combined with sinusoidal pulse width modulation (SPWM) approach is proposed to control the FESS charging and discharging. Different ship operating situations and the impact of the ship rolling is taken into consideration. The simulation results demonstrate the high efficiency and fast response of the flywheel energy storage system to enhance the stability of the proposed hybrid ship power system.

This study optimizes the tilt angle of photovoltaic (PV) panels on a large oil tanker ship system and considers the impact of partial shading to improve the performance of the PV system. This work presents a novel method that considers the difference between the expected and real outputs of PV modules to optimize the size of energy storage system (ESS). The method also takes into account the cost of wasted power, the capital cost of the system, fuel cost and the CO₂ emissions. Unlike on land, power generation using a PV on a ship depends on the date, latitude and longitude of the navigation. Accordingly, this work considers a route from Dalian in China to Aden in Yemen, accounting for the seasonal and geographical variations of solar irradiation. This proposed method adopts five conditions associated with the navigation route to model the total shipload. Various cases are discussed in detail to demonstrate the effectiveness of the proposed algorithm.

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.

With the increasing penetration of wind power, not only the uncertainties but also the correlation among the wind farms should be considered in the power system analysis. In this paper, Clayton-Copula method is developed to model the multiple correlated wind distribution and a new point estimation method (PEM) is proposed to discretize the multi-correlated wind distribution. Furthermore, combining the proposed modeling and discretizing method with Hybrid Multi-Objective Particle Swarm Optimization (HMOPSO), a comprehensive algorithm is explored to minimize the power system cost and the emissions by searching the best placements and sizes of energy storage system (ESS) considering wind power uncertainties in multi-correlated wind farms. In addition, the variations of load are also taken into account. The IEEE 57-bus system is adopted to perform case studies using the proposed approach. The results clearly demonstrate the effectiveness of the proposed algorithm in determining the optimal storage allocations considering multi-correlated wind farms.

Unbalanced voltage will inevitably cause power and DC voltage fluctuations in a three-phase PV system. The deterioration of power quality will do great harm to the PV panels and the loads, so it is necessary to suppress the power fluctuations. This paper further explores the coefficients control strategy of PV converters under unbalanced voltage conditions, aiming to suppress power fluctuations by controlling the injection of some specific orders of current harmonics into the grid. In order to achieve this, the current reference of the PV inverter has been changed by bringing in two control coefficients, and the expression of each order of the current harmonics has been deduced. Based on the standards of PV systems, the regions from which the coefficients can be selected are determined. Then, by tuning these coefficients in the feasible regions, the output parameters (power fluctuation, current THD and odd harmonics) can be controlled precisely. The model of this method is built and simulated in PSCAD/EMTDC, and as a result, it is shown that the power fluctuations can be restricted according to different power quality requirements.

A proper design of energy storage system (ESS) can effectively smooth the photovoltaic (PV) output power fluctuation, lower the cost, as well as improve power quality and stability. According to the response characteristics of different energy storage equipment, in this paper, a hybrid ESS is applied to make better use of each energy storage (ES) capacity and lower system cost. A sizing method of battery and supercapacitor is proposed based on frequency analysis. Fast Fourier transform (FFT) method is applied for analyzing the spectrum of balancing power, dividing the compensation frequency band for each ES device, and filtering. Then the power capacity and energy capacity of each ES can be determined by conducting inverse FFT within certain frequency range. Through the proposed method, the least cost ESS solution will be determined to smooth out the PV output fluctuation under a worst case scenario.