Assessment Method of Offshore Wind Resource Based on Multi-dimenssiional Indexes System

Traditional assessment indexes could not fully describe offshore wind resources,for the meteorological properties of offshore are more complex than onshore.As a result,the uncertainty of offshore wind power projects would be increased and final economic benefits would be affected.Therefore,a study on offshore wind resource assessment is carried out,including three processes of“studying data sources,conducting multidimensional indexes system and proposing an offshore wind resource assessment method based on analytic hierarchy process(AHP).First,measured wind data and two kinds of reanalysis data are used to analyze the characteristics and reliability of data sources.Second,indexes such as effective wind speed occurrence,affluent level occurrence,coefficient of variation,neutral state occurrence have been proposed to depict availability,richness,and stability of offshore wind resources,respectively.Combined with existing parameters(wind power density,dominant wind direction occurrence,water depth,distance to coast),a multidimensional indexes system has been built and on this basis,an offshore wind energy potential assessment method has been proposed.Furthermore,the proposed method is verified by the annual energy production of five offshore wind turbines and practical operating data of four offshore wind farms in China.This study also compares the ranking results of the AHP model to two multi-criteria decision making(MCDM)models including weighted aggregated sum product assessment(WASPAS)and multi-attribute ideal real comparative analysis(MAIRCA).Results show the proposed method gains well in practical engineering applications,where the economic score values have been considered based on the offshore reasonable utilization hours of the whole life cycle in China.

Local Observations-Based Energy-Efficient Multi-Cell Beamforming via Multi-Agent Reinforcement Learning

With affordable overhead on information exchange,energy-efficient beamforming has potential to achieve both low power consumption and high spectral efficiency.This paper formulates the problem of joint beamforming and power allocation for a multiple-input single-output(MISO)multi-cell network with local observations by taking the energy efficiency into account.To reduce the complexity of joint processing of received signals in presence of a large number of base station(BS),a new distributed framework is proposed for beamforming with multi-cell cooperation or competition.The optimization problem is modeled as a partially observable Markov decision process(POMDP)and is solved by a distributed multi-agent self-decision beamforming(DMAB)algorithm based on the distributed deep recurrent Q-network(D2RQN).Furthermore,limited-information exchange scheme is designed for the inter-cell cooperation to boost the global performance.The proposed learning architecture,with considerably less information exchange,is effective and scalable for a high-dimensional problem with increasing BSs.Also,the proposed DMAB algorithms outperform distributed deep Q-network(DQN)based methods and non-learning based methods with significant performance improvement.