The outbreak of COVID-19 pandemic has increased the production costs of renewable energy facilities and undermines the profitability of renewable energy investment. Green finance polices, e.g. carbon pricing, tradable green certificate (TGC) and green credit, can provide low-cost finances and counteract the adverse effects of COVID-19 pandemic. In this work, the generation costs of offshore wind power before and after the COVID-19 pandemic in China are analyzed using the data of 97 offshore wind power projects implemented in the period of 2014–2020, and the effect of green finance policy on the generation cost and the project profitability are evaluated. The results show that the average levelized cost of electricity (LCOE) of offshore wind power decreased from 0.86 CNY/kWh in 2014 to 0.72 CNY/kWh in 2019, while it increased to 0.79 CNY/kWh in 2020, i.e. 10.85% increase relative to that in 2019. With the average carbon price of 50 CNY/t CO
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, the average TGC price of 170 CNY and the green-credit policy being introduced, the average LCOE decreases to 0.76 CNY/kWh, 0.67 CNY/kWh and 0.74 CNY/kWh respectively. The green finance policy mix is still necessary to support the offshore wind power investment during the Covid-19 pandemic.
Grey wolf optimizer (GWO) is an efficient swarm intelligence algorithm for kinds of optimization problems. However, GWO tends to be trapped in local optimum when solving large-scale problems. Social hierarchy is one of the main characteristics of GWO which affect the searching efficiency. Thus, an improved algorithm called hierarchy strengthened GWO (HSGWO) is proposed in this paper. First, the pack of wolves is roughly divided into two categories: dominant wolves and omega wolves. Second, the enhanced elite learning strategy is performed for dominant wolves to prevent the misguidance of lowranking wolves and improve the collective efficiency. Then, the hybrid GWO and differential evolution (DE) strategy is executed for omega wolves to avoid falling into local optimum. In addition, a new hybrid one-dimensional and total-dimensional selection strategy is designed for omega wolves to balance the exploration and the exploitation during optimization. Finally, a perturbed operator is used to maintain the diversity of the population and further improve the exploration. To make a complete evaluation, the proposed HSGWO is first compared with six representative GWO variants for 50-dimensional problems based on CEC2014 benchmarks. The scalability of HSGWO is further tested by comparing it with eight state-of-theart non-GWO algorithms for large-scale optimization problems with 100 decision variables. In addition, feature selection problem is used for testing the effectiveness of HSGWO on real-world applications. The experimental results demonstrated that the proposed algorithm outperforms other algorithms in terms of solution quality and convergence rate in most of the experiments. INDEX TERMS Swarm intelligence algorithm, grey wolf optimizer (GWO), social hierarchy, numerical optimization, feature selection.
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