Can Modern Multi-Objective Evolutionary Algorithms Discover High-Dimensional Financial Risk Portfolio Tradeoffs for Snow-Dominated Water-Energy Systems?

Abstract

Hydropower-reliant power utilities are becoming increasingly vulnerable to hydrologic variability in states such as California, that have suffered from extensive droughts and reduced winter snowfall. One such utility is the San Francisco Public Utilities Commission (SFPUC), which operates the Hetch Hetchy Power System. SFPUC is strongly reliant on snowmelt from the Sierra Nevada to provide hydropower to San Francisco. Therefore, it is particularly financially vulnerable to changes in snowpack availability and timing, which translates to variability in yearly revenue. Evolutionary multi-objective direct policy search (EMODPS) can be used to identify time adaptive stochastic rules that inform optimal financial decisions based on state and exogenous information. However, the resulting financial risk mitigation portfolio planning problem is difficult to optimize due to its high dimensionality and mixture of nonlinear, nonconvex, and discrete objectives. We contribute a diagnostic assessment of state-of-the-art MOEAs’ abilities to support an EMODPS framework for managing financial risk. We perform comprehensive diagnostics on five algorithms: the Borg multi-objective evolutionary algorithm, Non-dominated Sorting Genetic Algorithm II (NSGA-II), Non-dominated Sorting Genetic Algorithm III (NSGA-III), Reference Vector Guided Evolutionary Algorithm (RVEA), and the Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D). MOEA performance is evaluated by analyzing controllability, reliability, efficiency, and effectiveness. The results emphasize the importance of using MOEAs with archiving and adaptive search capabilities in order to solve complex financial risk portfolio problems in snow dependent water-energy systems.