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We present and benchmark an approximate dynamic programming algorithm that is capable of designing near-optimal control policies for time-dependent, finite-horizon energy storage problems, where wind supply, demand and electricity prices may evolve stochastically. We found that the algorithm was able to design storage policies that are within 0.08% of(More)
—As more renewable, yet volatile, forms of energy like solar and wind are being incorporated into the grid, the problem of finding optimal control policies for energy storage is becoming increasingly important. These sequential decision problems are often modeled as stochastic dynamic programs, but when the state space becomes large, traditional (exact)(More)
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