Hybrid Wind/PV E-Bike Charging Station: Comparison of Onshore and Offshore Systems

Author:

Afzal Wardah1,Zhao Li-Ye1ORCID,Chen Guang-Zhi1,Xue Yu1

Affiliation:

1. College of Engineering, Ocean University of China, Qingdao 266100, China

Abstract

The concept behind this research article is advancement towards utilizing renewable energy sources of wind–solar to generate electrical energy for E-bike (electric bike) charging stations. To optimize the design and operation control of the wind–solar E-bike charging station system, the development of modelling this hybrid power generation system, consisting of solar and wind energy combined with battery storage, is proposed and will be studied in this paper. A university campus setting is utilized for the case study by comparing offshore (Huangdao) and onshore (Laoshan) sites. The proposed research will focus on annual energy production (AEP) and system cost analysis. The proposed work’s main objectives are to analyze the wind/solar properties of the installation’s location using the last 20 years’ data, calculate the AEP for wind turbines and solar PV, and estimate how many E-bikes can be charged day/year with reliable operation. We have calculated that the hybrid power available is 27.08 kWh/day offshore and 22 kWh/day onshore. This research study concludes that on average, based on AEP, in the case of offshore, 5110 E-bikes can be charged per year and in the case of onshore, 4015 E-bikes can be charged per year. We have also calculated the COE (cost of energy) for 20 years for the proposed project, which is $0.62/kWh onshore and $0.46/kWh offshore.

Funder

special fund for offshore wind power intelligent measurement and control research center and laboratory construction at Ocean University of China

Publisher

MDPI AG

Subject

Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction

Reference30 articles.

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4. Jaruwatanachai, P., Sukamongkol, Y., and Samanchuen, T. (2023). Predicting and Managing EV Charging Demand on Electrical Grids: A Simulation-Based Approach. Energies, 16.

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