A Distributed and Resilient Bargaining Game for Weather-Predictive Microgrid Energy Cooperation

TL;DR

This paper introduces a distributed bargaining game for microgrid energy cooperation that integrates weather-based renewable prediction, ensuring resilient and fair power cost allocation among users.

Contribution

It presents a novel combination of a distributed power scheduling algorithm with a Nash Bargaining Solution, incorporating weather predictions and resilience to malicious users.

Findings

Weather prediction improves day-ahead scheduling accuracy.

The bargaining game is resilient to dishonest user behavior.

The proposed method effectively allocates costs fairly among users.

Abstract

A bargaining game is investigated for cooperative energy management in microgrids. This game incorporates a fully distributed and realistic cooperative power scheduling algorithm (CoDES) as well as a distributed Nash Bargaining Solution (NBS)-based method of allocating the overall power bill resulting from CoDES. A novel weather-based stochastic renewable generation (RG) prediction method is incorporated in the power scheduling. We demonstrate the proposed game using a 4-user grid-connected microgrid model with diverse user demands, storage, and RG profiles and examine the effect of weather prediction on day-ahead power scheduling and cost/profit allocation. Finally, the impact of users' ambivalence about cooperation and /or dishonesty on the bargaining outcome is investigated, and it is shown that the proposed game is resilient to malicious users' attempts to avoid payment of their fair share of the overall bill.