Investigation of Choice of State Space for Reinforcement Learning Based Battery Scheduling in Microgrid

P, Imthias Ahamed T; Roy, Rahul; Siyad, Sajin; Santhosh, Sarin; Rajeev, Vaishak

doi:10.1109/spices52834.2022.9774140

Cited by 1 publication

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“…However, only fixed tariffs have been considered in their work. Other optimization methods include dynamic programming [22], quadratic programming [23,24], genetic algorithm [25,26], particle swarming [26][27][28][29], honey bee mating [30], as well as machine learning techniques, most notably reinforcement learning [31][32][33].…”

Section: Related Workmentioning

confidence: 99%

Improving the Efficiency of Renewable Energy Assets by Optimizing the Matching of Supply and Demand Using a Smart Battery Scheduling Algorithm

et al. 2023

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Given the fundamental role of renewable energy assets in achieving global temperature control targets, new energy management methods are required to efficiently match intermittent renewable generation and demand. Based on analysing various designed cases, this paper explores a number of heuristics for a smart battery scheduling algorithm that efficiently matches available power supply and demand. The core of improvement of the proposed smart battery scheduling algorithm is exploiting future knowledge, which can be realized by current state-of-the-art forecasting techniques, to effectively store and trade energy. The performance of the developed heuristic battery scheduling algorithm using forecast data of demands, generation, and energy prices is compared to a heuristic baseline algorithm, where decisions are made solely on the current state of the battery, demand, and generation. The battery scheduling algorithms are tested using real data from two large-scale smart energy trials in the UK, in addition to various types and levels of simulated uncertainty in forecasts. The results show that when using a battery to store generated energy, on average, the newly proposed algorithm outperforms the baseline algorithm, obtaining up to 20–60% more profit for the prosumer from their energy assets, in cases where the battery is optimally sized and high-quality forecasts are available. Crucially, the proposed algorithm generates greater profit than the baseline method even with large uncertainty on the forecast, showing the robustness of the proposed solution. On average, only 2–12% of profit is lost on generation and demand uncertainty compared to perfect forecasts. Furthermore, the performance of the proposed algorithm increases as the uncertainty decreases, showing great promise for the algorithm as the quality of forecasting keeps improving.

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