BOOST: Microgrid Sizing using Ordinal Optimization

TL;DR

BOOST introduces a hybrid optimization method combining ordinal optimization and MILP to efficiently size residential microgrids, improving accuracy and reducing runtime on synthetic benchmarks.

Contribution

The paper presents a novel hybrid optimization framework for microgrid sizing that outperforms baseline methods in accuracy and efficiency, validated on synthetic datasets.

Findings

BOOST achieves 51.8% runtime reduction compared to exhaustive evaluation.

The best design on the synthetic dataset is a 500 kWh battery with 1833.3 kW PV.

LP and MILP rankings are effectively identical (rho = 1.000).

Abstract

Sizing a residential microgrid efficiently requires solving a coupled design-and-operation problem: photovoltaic (PV) and battery capacities should be chosen in a way that reflects how the system will actually be dispatched over time. This paper proposes BOOST, or Battery-solar Ordinal Optimization Sizing Technique, which combines ordinal optimization (OO) with mixed-integer linear programming (MILP). OO is used to screen a large set of candidate battery/PV designs with a simple linear model and then re-evaluate only the most promising designs with a more accurate MILP that captures diesel commitment logic. Relative to the original short paper, this expanded manuscript retains the full methodological narrative but refreshes the quantitative section using a new synthetic benchmark dataset suite generated from the released clean reimplementation. The suite contains five yearly synthetic datasets/configurations: base, cheap battery, cheap PV, expensive diesel, and high peak tariff. On the base synthetic dataset, the best accurate design is a 500 kWh battery with 1833.3 kW of PV, achieving 13.169 c/kWh, while BOOST improves upon dynamic programming and greedy baselines. Across the full 10 x 10 design grid, the LP and MILP rankings are effectively identical (rho = 1.000), the paper-style choice of N = 90 and s = 18 recovers the global accurate optimum, and the OO-based workflow reduces runtime by 51.8% relative to exhaustive accurate evaluation on the refreshed synthetic benchmark run. Because these added datasets are synthetic, they should be read as methodological stress tests rather than as direct empirical claims about any specific real-world site. Code is available at https://github.com/MFHChehade/Microgrid-Optimization.