Distributed Multi-Step Power Scheduling and Cost Allocation for Cooperative Microgrids
For microgrid operators and researchers, this work addresses the need for fully-distributed, multi-step energy scheduling with fair cost allocation, but the results are limited to a small example and lack quantitative performance comparisons.
This paper extends a distributed multi-step scheduling algorithm (CoDES) for microgrids to include power trading with the main grid, and proposes a Nash Bargaining Solution-based method for fair cost allocation among agents. The approach is demonstrated on a three-agent microgrid example, analyzing the impact of price schedules and user activity levels.
Microgrids are self-sufficient small-scale power grid systems that can employ renewable generation sources and energy storage devices and can connect to the main grid or operate in a stand-alone mode. Most research on energy-storage management in microgrids does not take into account the dynamic nature of the problem and the need for fully-distributed, multi-step scheduling. First, we address these requirements by extending our previously proposed \textit{multi-step cooperative distributed energy scheduling} (CoDES) algorithm to include both purchasing power from and selling the generated power to the main grid. Second, we model the microgrid as a multi-agent system where the agents (e.g. households) act as players in a cooperative game and employ a distributed algorithm based on the Nash Bargaining Solution (NBS) to fairly allocate the costs of cooperative power management (computed using CoDES) among themselves. The dependency of the day-ahead power schedule and the costs on system parameters, e.g., the price schedule and the user activity level (measured by whether it owns storage and renewable generation devices), is analyzed for a three-agent microgrid example.