This paper proposes a frequency-based method for sizing the hybrid energy storage system in order to smoothen wind power fluctuations. The main goal of the proposed method is to find the power and energy capacities of the hybrid energy storage system that minimizes the total cost per day of all the systems. The energy management strategy used in this paper is designed as a two-level energy distribution scheme: the first level is responsible for setting the output power of hybrid energy storage system, the second level manages the power flow between the battery and supercapacitor. The hybrid parallel particle swarm optimization-genetic algorithm (PSO-GA) optimization algorithm is proposed to solve the control parameters of energy management strategy. In addition, the proposed method uses the piecewise fitting function to describe the lifetime of battery. Obtained results show that the hybrid energy storage system with the proposed energy management strategy is able to offer the best performances for the wind power system in terms of cost and lifetime.

An experimental study regarding methanol–diesel dual-fuel (DF) engines was conducted on a modified engine to explore the effects of pilot injection timing and period on the two-stage combustion process caused by the pilot injection strategy. In this study, the two-stage combustion process was determined according to the first two peaks of the second derivative of an in-cylinder pressure (d²p/dφ²) curve. The results show that the peak pressure rise rate (PRR) tended to decrease with advancing pilot injection timing at a high co-combustion ratio (CCR), which reduced combustion noise. The start of the combustion of the main injection diesel (SOC2) could be advanced by increasing the pilot injection period or advancing pilot injection timing at a 42% CCR. At an 18% CCR, the pilot injection timing and period had no significant effect on SOC2. With the advancement of pilot injection timing, the start of the combustion of pilot injection diesel (SOC1) advanced, and generally, the coefficient of variation of the PRR (COV_PRR) of the two-stage combustion process increased first and then decreased. However, with the increase in the pilot injection period, SOC1 almost always remained constant and the COV_PRR of the two-stage combustion process generally increased.

The traditional decomposition–combination wind speed forecasting model has high complexity and a long calculation time. As a result, an ultra-short-term wind speed hybrid forecasting model based on a broad learning system (BLS) that combines improved variational mode decomposition (EPSO-VMD, EVMD) and subseries reconstruction (SR) is proposed in this work. The values of K and α in the EVMD are determined by minimum mean envelope entropy (MMEE) and enhanced particle swarm optimization (EPSO), and EVMD is used to decompose the original wind speed data. SR is applied to recombine the subseries obtained by EVMD to improve the forecasting efficiency. The sample entropy (SE) is used to quantify the subseries’ complexity, and they are then adaptively divided into high-entropy and low-entropy subseries. Adjacent high-entropy subseries of approximate entropy values are merged to obtain a new group of reconstructed high-entropy subseries, while the low-entropy subseries merge into a new subseries as well. Then, the forecasting results of the reconstructed high- and low-entropy subseries are calculated via the BLS and ARIMA models. Numerical simulation results show that the proposed method is more effective than traditional methods.

The sizing of hybrid energy storage system (HESS) for smoothing wind power fluctuations was studied in this paper. The goal of the proposed method is to find the optimal sizing of the battery and supercapacitor, and its meet the constraints of the power ramp rate constraint and the economic cost. Therefore, a power allocation strategy which based on the operating period of battery and the cutoff frequency of battery absorption power was proposed. The sizing optimization model, which considered the cost structure of HESS, the ESS characteristics and cycle life in different operating modes, is established by using minimum daily cost as objective function. The results of numerical example shows that the cycle life of ESS can be effectively extended with reasonable operating period of battery and the cutoff frequency of battery absorption power, the cost of HESS can be reduced.