Simulation-Based Optimization of Hybrid Renewable Energy System for Off-grid Rural Electrification

There is a need to develop an optimization tool that can be applied in the feasibility study of a hybrid renewable energy system to find the optimal capacity of different renewable energy resources and support the decision makers in their performance investigation. A multi-objective function which minimizes the Levelized Cost of Energy (LCOE) and Loss of Load Probability Index (LLPI) but maximizes the novel Energy Match Ratio (EMR) was formulated. Simulation-based optimization method combined with epsilon-constraint technique was developed to solve the multi-objective optimization problem. In the study, ten-year hourly electrical load demand, using the end-use model, is estimated for the communities. The performance of the developed algorithm was evaluated and validated using Hybrid Optimization Model for Electric Renewables (HOMER (R)) optimization software. The developed algorithm minimized the LCOE by 6.27% and LLPI by 167% when compared with the values of LCOE ($0.444/kWh) and LLPI (0.000880) obtained from the HOMER (R) optimization tool. Also. the LCOE with the proposed approach was calculated at $0.417/kWh, which is lower than the $0.444/kWh obtained from HOMER (R). From environmental perspective, it is found that while 141,370.66 kg of CO2 is saved in the base year, 183,206.51 kg of CO2 is saved in the ninth year. The study concluded that the approach is computationally efficient and performed better than HOMER (R) for this particular problem. The proposed approach could be adopted for carrying out feasibility studies and design of HRES for Off-Grid electrification, especially in the rural areas where access to the grid electricity is limited.