Stochastic distributed microgrid energy management based on over-relaxed alternative direction method of multipliers

Abstract

This study aims to design a stochastic distributed energy management framework taking into account the information privacy of autonomous agents. To develop this framework, first, the application of a scenario-based method on the predicted probability density function (PDF) is suggested to deal with uncertainties of the low-scale loads, renewable generations, i.e. wind and photovoltaic generations, and electricity market prices. Then, an alternative direction method of multipliers (ADMM) based method, namely over-relaxed ADMM, is presented to optimise the operational set points considering each agent benefits and technical constraints. In this framework, the agents participate in scheduling programs without sharing influential information and corresponding historical data. The presented framework is tested on a realistic small-scale microgrid (MG) system and real historical data. The performance and efficiency are verified by comparison of the proposed over-relaxed ADMM method application with the application of standard ADMM and analytic targeting cascading in terms of accuracy and convergence speed. Furthermore, higher accuracy and lower computational complexity of predictive PDF-based scenario generation techniques in distributed MG energy management are verified by comparison with distributed energy management based on predefined PDF and historical data.