Abstract
In this study, we develop a load estimation method and an optimization tool for community-driven planning of rural electricity systems which aims to encourage stakeholder involvement in planning processes and reinforce the sustainability of small-scale electrification projects. Electricity demand is estimated through the bottom-up construction of load profiles based on devices used in three common rural end-use sectors. A cost minimization model is then implemented to determine the least-cost capacity composition that can be installed based on the load profile and energy availability. The energy sources modeled are small-scale wind, hydro, solar (photovoltaic), diesel, and battery. In the base case, which includes the three sectors equally, most of the optimal capacity (77%) is provided by renewable energy at an average levelized cost of electricity (LCOE) of $0.05/kWh for a notional village with 500 houses. The base case results are compared to the results when each sector is respectively favored. The results show that backup dispatchable generation and batteries can both be solutions to intermittent renewables, and the choice between the two appears to depend on the load shape. We also find that the base case results are not very sensitive to the CO2 tax, suggesting that not only are renewables cost-competitive with or without the tax, but they also benefit economically from coupling with cheap fossil fuel generators.
A rule-based energy management scheme for long-term optimal capacity planning of grid-independent microgrid optimized by multi-objective grasshopper optimization algorithm