Autonomous Demand-Side Management system based on Monte Carlo Tree Search

Smart Grid (SG) technologies are becoming increasingly dynamic, motivating the use of computational intelligence to support the SG by predicting and intelligently responding to certain requests (e.g, reducing electricity costs given fluctuating prices). The presented work intends to do precisely this, to make intelligent decisions to switch on electric devices at times when the electricity price (prices that change over time) is the lowest while at the same time attempting to balance energy usage by avoiding turning on multiple devices at the same time, whenever possible. To this end, we use Monte Carlo Tree Search (MCTS), a real-time decision algorithm. MCTS takes into consideration what might happen in the future by approximating what other entities/agents (electric devices) might do via Monte Carlo simulations. We propose two variants of this method: (a) maxⁿ MCTS approach where the competition for resources (e.g, lowest electricity price) happens in one single decision tree and where all the devices are considered, and (b) two-agent MCTS approach, where the competition for resources is distributed among various decision trees. To validate our results, we used two scenarios, a rather simple one where there are no constraints associated to the problem, and another more complex, and realistic scenario with equality and inequality constraints associated to the problem. The results achieved by this real-time decision tree algorithm are very promising, specially those achieved by the maxⁿ MCTS approach.