Abstract
Recently, many renewable energy (RE) initiatives around the world are based on general frameworks that accommodate the regional assessment taking into account the mismatch of supply and demand with pre-set goals to reduce energy costs and harmful emissions. Hence, relying entirely on individual assessment and RE deployment scenarios may not be effective. Instead, developing a multi-faceted RE assessment framework is vital to achieving these goals. In this study, a regional RE assessment approach is presented taking into account the mismatch of supply and demand with an emphasis on Photovoltaic (PV) and wind turbine systems. The study incorporates mapping of renewable resources optimized capacities for different configurations of PV and wind systems for multiple sites via test case. This approach not only optimizes system size but also provides the appropriate size at which the maximum renewable energy fraction in the regional power generation mix is maximized while reducing energy costs using MATLAB’s ParetoSearch algorithm. The performance of the proposed approach is tested in a realistic test site, and the results demonstrate the potential for maximizing the RE share compared to the achievable previously reported fractions. The results indicate the importance of resource mapping based on energy-demand matching rather than a quantitative assessment of anchorage sites. In the examined case study, the new assessment approach led to the identification of the best location for installing a hybrid PV / wind system with a storage system capable of achieving a nearly 100% autonomous RE system with Levelized cost of electricity of 0.05 USD/kWh.
Highlights
The energy market witnessed a significant decline in the adaptation of distributed energy resources (DERs) such as wind turbines and solar Photovoltaics (PV) due to the impacts of the novel COVID-19 on the world’s economy
The optimal renewable energy system (RES) capacities with and without ESS were found at each location using multi-objective optimization based on maximizing the contribution of RES to local demand and the demand-supply fraction while minimizing the cost of supplied energy, to assess the advantage of the presented mapping
The optimal RES capacities with and without ESS were found at each location using multi-objective optimization based on maximizing the RES fraction and the demand-supply fraction while minimizing the LCOE
Summary
The energy market witnessed a significant decline in the adaptation of distributed energy resources (DERs) such as wind turbines and solar Photovoltaics (PV) due to the impacts of the novel COVID-19 on the world’s economy. Energy demand has dropped down in the industrial and commercial sectors, in contrary, the load. The associate editor coordinating the review of this manuscript and approving it for publication was Dipankar Deb. increased in the residential sector. Elavarasan et al [1] studied the impact of COVID-19 pandemic on the power sector for the Indian power grid. Their work investigated global scenarios along with the social-economic and technical issues encountered by utilities. The same article emphasizes that the economic advantages of clean energy production methods possess a long-term value compared to fossil-fuel-based
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