A signaling game-optimization algorithm for residential energy communities implemented at the edge-computing side

•Optimizing the load by minimizing the cost for residential communities.•A signalling game-optimization algorithm.•Advanced tariffs implementation.•Savings, gain, flattening index and PAR. The ongoing tremendous progress of IT&C technologies, large-scale integration of smart metering systems, along with smart appliances and energy management systems allow the implementation of various advanced tariff scheme such as: Time-of-Use (ToU), critical peak pricing, real-time tariff (RTT), etc. These new features enhance suppliers to interact with consumers and provide incentives for peak shaving. Thus, the electricity consumers schedule the operation of their appliances to minimize the electricity cost. Game theory is applied to various economic processes, including electricity consumption optimization, leading to significant savings. Hence, in this paper, we propose a Signaling Game model for Optimization (SGO), that is performed at the Edge-computing node and involves multiple optimization steps. An advanced tariff signal is iteratively computed based on the consumption level. The game-optimizing process stops when there is no further improvement of the players’ reward. The simulations using advanced tariffs and a one-year dataset of a small residential community of several houses reveal excellent results in terms of savings, gains, Flat Index (FI) and Peak Average to Ratio (PAR). The gain of the community is about 11% and the savings are approx. 3,357 Euro per year. Compared to the results with a ToU tariff, the results with advanced tariffs (RTT) are significantly improved: FI increased from 0.68 to 0.76 and PAR decreased from 2.3 to 1.86.