Distributed Multi-Step Power Scheduling and Cost Allocation for Cooperative Microgrids

TL;DR

This paper develops a distributed multi-step energy scheduling algorithm for microgrids that incorporates grid interactions and fair cost allocation among agents, addressing the dynamic and cooperative nature of microgrid management.

Contribution

It extends previous scheduling algorithms to include grid buying/selling and introduces a game-theoretic cost allocation method for cooperative microgrid management.

Findings

The extended CoDES algorithm effectively manages multi-step scheduling with grid interactions.

The Nash Bargaining Solution provides a fair cost distribution among microgrid agents.

System parameters significantly influence scheduling and cost outcomes.

Abstract

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.