Microgrid Energy Management With Asynchronous Decentralized Particle Swarm Optimization

Abstract

Controlling distributed energy resources (DERs) in low voltage microgrids is a challenging task for operators. The simultaneous operation of independent small-scale DER owners could compromise the operator's hierarchical and centralized control to reach system stability and cost optimization. Recent Decentralized Energy Management (DEM) approaches provide flexibility for DERs control, but several existing solutions depend on powerful and expensive computer clusters and their ability to deal with a high burden of data in the communication channel. This work is motivated towards a DEM framework that involves independent DER owners while microgrid operator still maintains a hierarchical control philosophy. The framework must include a method to reduce the need of powerful computer clusters and depend on low bandwidth communications channel. Here, a multi-layered framework for every DER, consisting of physical, control, and agent layers for DEM is approached, where the agent layer participates in the energy management task. An Asynchronous Decentralized PSO (ADPSO) algorithm is proposed for the agent layer based on its primal characteristic: it can reach a consensus state between networked computing units by exchanging asynchronously only the state variable through the communications channel. The proposed solution allows the integration of DEM capabilities within the physical controller of the DERs, distinguishing it from other decentralized solutions. Easiness of implementation and low computational requirements are shown by performing DEM tests on single board computers. The tests show improved convergence rate, improved swarm diversity behavior and fast consensus reaching of DEM optimization.

Energy Management of Hybrid DC Microgrid with Different Levels of DC Bus Voltage for Various Load Types

“…This study uses model predictive control to cut down on daily operating costs by reducing the error that occurs between the prediction of load and the generation of power. Energy management is optimized for the day ahead by using intraday rolling horizon energy management and optimizing control values to compensate for errors in forecasting [21]. Multiple battery energy management systems for a microgrid are presented in [22] that rely primarily on photovoltaic and diesel generators as they are primary sources of electricity.…”