The methodologies above require a level of expertise in algorithms and programming to be able to directly apply to a rural electrification project. Because of this, there are software tools that take care of applying the algorithms out of the user input parameters.
These tools can be characterized according to the optimization technique, energy mix portfolio, grid consideration, geographic coverage, output variable/s to optimize, output performance metrics, model complexity, availability and necessary software, and options to handle risk or different scenarios.
The table below lists different software tools and the optimization algorithm they employ. Calliope and Distributed Energy Resource-Customer Adoption Model (DER-CAM) employ mixed integer linear programming that is solved using either open or commercial solvers. Open and free solvers include CBC and GPLK while commercial solvers are Gurobi and CPLEX. Hybrid Optimization of Multiple Energy Resources (HOMER) uses a proprietary derivative-free algorithm to arrive at the optimal energy mix. HOMER is generally classified as a simulation-based model instead of the strictly mathematical programming optimization technique[1] . Moreover, as the name suggests, improved Hybrid Optimization by Genetic Algorithm (iHOGA) applies Genetic Algorithm in sizing energy resources. The Reference Electrification Model (REM) applies a Pattern search-based method with a master-slave decomposition where discrete variables such as generator size are at the master level and continuous variables such as solar and battery sizes are at the slave level.
| Software Tool | Optimization technique |
| Calliope | Mixed Integer Linear Programming – Open and commercial solvers |
| DER-CAM | Mixed Integer Linear Programming – Open and commercial solvers |
| HOMER | Simulation, proprietary derivative-free |
| iHOGA | Genetic algorithm |
| REM | Pattern search – based |
[1] Cardoso, Gonçalo, et al. "The impact of ancillary services in optimal DER investment decisions." Energy 130 (2017): 99-112.