Optimal scheduling of deferrable demands with colocated stochastic supply and piecewise-linear pricing reduces to a finite set of three procrastination thresholds per demand class; a reinforcement learning algorithm learns these thresholds when distributions are unknown.
Renewable-Colocated Green Hydrogen Production: Optimal Scheduling and Profitability
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abstract
We study the optimal green hydrogen production and energy market participation of a renewable-colocated hydrogen producer (RCHP) that utilizes onsite renewable generation for both hydrogen production and grid services. Under deterministic and stochastic profit-maximization frameworks, we analyze RCHP's multiple market participation models and derive closed-form optimal scheduling policies that dynamically allocate renewable energy to hydrogen production and electricity export to the wholesale market. Analytical characterizations of the RCHP's operating profit and the optimal sizing of renewable and electrolyzer capacities are obtained. We use real-time renewable generation and electricity price data from three independent system operators to evaluate the impacts of market prices and environmental policies on RCHP's profitability.
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Joint Scheduling of Deferrable and Nondeferrable Demand with Colocated Stochastic Supply
Optimal scheduling of deferrable demands with colocated stochastic supply and piecewise-linear pricing reduces to a finite set of three procrastination thresholds per demand class; a reinforcement learning algorithm learns these thresholds when distributions are unknown.