Abstract
Can we automate the energy exchange of a power trader? To address this challenge, we present the Meta Agent Learner (MAL). The MAL is a tiered and multi-policy energy trader. It comprises data analytics (DA), a deep sequence-to-sequence recurrent neural network (DS2S) and reinforcement learning (RL). The DA phase draws knowledge out of the sheer flow of data. The DS2S phase creates wisdom and provides the intelligence for decision making. The RL phase senses and learns from the market to act strategically. We demonstrate the MAL in a scenario of a price-taker wind farm with a hydro plant. The testbed is real data from the NordPool and East Denmark (DK2). More specifically, electricity consumption, wholesale and balancing prices, cross border energy exchange, and weather conditions. The MAL optimizes the combined production of the wind farm and hydro pumped storage. Runs the hydro plant such that spillage of wind power is avoided or stores cheap market electricity. The performance is benchmarked with three traders.
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