When Does Global Search Pay for Itself? A Measured Exploration Cost and Energy Break-Even Analysis of a Metaheuristic Search Controller in Solar Energy Systems

Authors

• mohamed ali muammar ezgour, International Burch UniversityFollow

Abstract

Autonomous energy systems increasingly delegate the choice of operating point to embedded search algorithms, trading a fast local optimizer that can settle on a wrong point against a slower global search that guarantees the right one at a measurable cost. This paper reframes maximum power point tracking under partial shading as that decision and measures its economics on a fixed photovoltaic plant in MATLAB/Simulink. A Hippopotamus Optimization global search handed over to incremental conductance is compared with incremental conductance alone across seventeen initial duty cycles and thirty random seeds. The hybrid reached the global peak in all thirty seeds, whereas the local controller was trapped at about 72.9% of the available power from the upper third of its operating range (Fisher exact, p = 0.004). The search time, 55.80 s, is fixed by the plant rather than the algorithm and shows zero variance across seeds. Against a local controller that would have succeeded, the search is a permanent energy cost of about 5.6 kJ; against one that would have been trapped, the hybrid reaches cumulative-energy break-even at 59.90 s and then leads by a steady 92 W. The outcome is a decision rule, not a claim of universal superiority.

Recommended Citation

ezgour, mohamed ali muammar (2026) "When Does Global Search Pay for Itself? A Measured Exploration Cost and Energy Break-Even Analysis of a Metaheuristic Search Controller in Solar Energy Systems," Communications of the IIMA: Vol. 24: Iss. 1, Article 15.
DOI: https://doi.org/10.58729/1941-6687.1520
Available at: https://scholarworks.lib.csusb.edu/ciima/vol24/iss1/15