Fuzzy Leaky Bucket System for Intelligent Management of Consumer Electricity Elastic Load in Smart Grids - Alamaniotis, Miltiadis (Frontiers in Artificial Intelligence, 2020)
This paper frames itself in an informational rich smart electricity grid where consumers have access to various streams of information and make decisions over their daily consumption pattern. In particular, a new intelligent management system to accommodate possible optimal decisions for elastic load consumption is discussed. The energy management system implements a fuzzy driven leaky bucket that manages the elastic load of a consumer by controlling the token rate buffer via a set of four fuzzy variables (among them the electricity price). The goal of this innovative system is to allow loads that are identified as elastic to be scheduled only when it is potentially beneficial to the consumer. To that end, a fuzzy algorithm comprised of a set of rules is developed to manage the token rate of the leaky bucket and through that the decisions over the fate of elastic loads. The developed system is applied on a set of real-world electricity consumption data taken from a residential consumer, and benchmarked against a full scheduling method, where the elastic load is fully scheduled offline. Results exhibit that the proposed fuzzy logic method outperforms the full scheduling method in the vast majority of the cases, i.e. over 79% of the cases with respect to consumption cost. Furthermore, they validate its ability to conduct real time decision making with no human in the loop.