A Demand-Supply Matching-Based Approach for Mapping Renewable Resources Towards 100% Renewable Grids in 2050

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.

Automated summary

This study presents a regional RE assessment approach focusing on the mismatch of supply and demand, optimizing PV and wind systems through resource mapping to maximize renewable energy fraction. The results demonstrate the importance of resource mapping based on energy-demand matching rather than a quantitative assessment of anchorage sites.