Search Results (3)

Because the new energy is intermittent and uncertain, it has an influence on the system’s output power stability. A hydrogen energy storage system is added to the system to create a wind, light, and hydrogen integrated energy system, which increases the utilization rate of renewable energy while encouraging the consumption of renewable energy and lowering the rate of abandoning wind and light. Considering the system’s comprehensive operation cost economy, power fluctuation, and power shortage as the goal, considering the relationship between power generation and load, assigning charging and discharging commands to storage batteries and hydrogen energy storage, and constructing a model for optimal capacity allocation of wind–hydrogen microgrid system. The optimal configuration model of the wind, solar, and hydrogen microgrid system capacity is constructed. A particle swarm optimization with dynamic adjustment of inertial weight (IDW-PSO) is proposed to solve the optimal allocation scheme of the model in order to achieve the optimal allocation of energy storage capacity in a wind–hydrogen storage microgrid. Finally, a microgrid system in Beijing is taken as an example for simulation and solution, and the results demonstrate that the proposed approach has the characteristics to optimize the economy and improve the capacity of renewable energy consumption, realize the inhibition of the fluctuations of power, reduce system power shortage, and accelerate the convergence speed. Full article

Global Navigation Satellite System (GNSS)-Reflectometry (GNSS-R) technology has opened a new window for ocean remote sensing because of its unique advantages, including short revisit period, low observation cost, and high spatial-temporal resolution. In this article, we investigated the potential of estimating swell height from delay-Doppler maps (DDMs) data generated by spaceborne GNSS-R. Three observables extracted from the DDM are introduced for swell height estimation, including delay-Doppler map average (DDMA), the leading edge slope (LES) of the integrated delay waveform (IDW), and trailing edge slope (TES) of the IDW. We propose one modeling scheme for each observable. To improve the swell height estimation performance of a single observable-based method, we present a data fusion approach based on particle swarm optimization (PSO). Furthermore, a simulated annealing aided PSO (SA-PSO) algorithm is proposed to handle the problem of local optimal solution for the PSO algorithm. Extensive testing has been performed and the results show that the swell height estimated by the proposed methods is highly consistent with reference data, i.e., the ERA5 swell height. The correlation coefficient (CC) is 0.86 and the root mean square error (RMSE) is 0.56 m. Particularly, the SA-PSO method achieved the best performance, with RMSE, CC, and mean absolute percentage error (MAPE) being 0.39 m, 0.92, and 18.98%, respectively. Compared with the DDMA, LES, TES, and PSO methods, the RMSE of the SA-PSO method is improved by 23.53%, 26.42%, 30.36%, and 7.14%, respectively. Full article

Spatial analysis of hydrological data often requires the interpolation of a variable from point samples. Commonly used methods for solving this problem include Inverse Distance Weighting (IDW) and Kriging (KG). IDW is easily extensible, has a competitive computational cost with respect to KG, hence it is usually preferred for this task. This paper proposes the optimization of finding the IDW parameter using a nature-inspired metaheuristic, namely Particle Swarm Optimization (PSO). The performance of the improved algorithm is evaluated in a complex scenario and benchmarked against the KG algorithm for 51 precipitation series from the Dobrogea region (Romania). Apart from facilitating the process of applying IDW, the PSO implementation for Optimizing IDW (OIDW) is computationally lighter than the traditional IDW approach. Compared to Kriging, OIDW is straightforward to be implemented and does not require the difficult process of identification of the most appropriate variogram for the given data. Full article