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Modern power systems will have to face difficult challenges in the years tocome: frequent blackouts in urban areas caused by high power demand peaks, gridinstability exacerbated by intermittent renewable generation, and globalclimate change amplified by rising carbon emissions. While current practicesare growingly inadequate, the path to widespread adoption of artificialintelligence (AI) methods is hindered by missing aspects of trustworthiness.The CityLearn Challenge is an exemplary opportunity for researchers frommultiple disciplines to investigate the potential of AI to tackle thesepressing issues in the energy domain, collectively modeled as a reinforcementlearning (RL) task. Multiple real-world challenges faced by contemporary RLtechniques are embodied in the problem formulation. In this paper, we present anovel method using the solution function of optimization as policies to computeactions for sequential decision-making, while notably adapting the parametersof the optimization model from online observations. Algorithmically, this isachieved by an evolutionary algorithm under a novel trajectory-based guidancescheme. Formally, the global convergence property is established. Our agentranked first in the latest 2021 CityLearn Challenge, being able to achievesuperior performance in almost all metrics while maintaining some key aspectsof interpretability.

Winning the CityLearn Challenge: Adaptive Optimization with Evolutionary Search under Trajectory-based Guidance