Negotiation-based task scheduling to minimize user's electricity bills under dynamic energy prices

Dynamic energy pricing is a promising technique in the Smart Grid that incentivizes energy consumers to consume electricity more prudently in order to minimize their electric bills meanwhile satisfying their energy requirements. This has become a particularly interesting problem with the introduction of residential photovoltaic (PV) power generation facilities. This paper addresses the problem of task scheduling of (a collection of) energy consumers with PV power generation facilities, in order to minimize the electricity bill. A general type of dynamic pricing scenario is assumed where the energy price is both time-of-use and total power consumption-dependent. A negotiation-based iterative approach has been proposed that is inspired by the state-of-the-art Field-Programmable Gate Array (FPGA) routing algorithms. More specifically, the negotiation-based algorithm is used to rip-up and re-schedule all tasks in each iteration, and the concept of congestion is effectively introduced to dynamically adjust the schedule of each task based on the historical scheduling results as well as the (historical) total power consumption in each time slot. Experimental results demonstrate that the proposed algorithm achieves up to 51.8% improvement in electric bill reduction compared with baseline methods.