Real-time management solutions for a smart polygeneration microgrid. (15th March 2016)

• Record Type: Journal Article
• Authors: Rossi, Iacopo; Banta, Larry; Cuneo, Alessandra; Ferrari, Mario Luigi; Traverso, Alberto Nicola; Traverso, Alberto
• Is Part Of: Energy conversion and management. Volume 112(2016)
• Page Start: 11
• Page End: 20
• Publication Date: 2016-03-15
• Subjects: Smart grid -- Cogeneration -- Experimental analysis -- Control systems -- Real-time optimization; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2015.12.026
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: Different management systems are compared on the basis of experimental results. Target system is a real smart microgrid composed by different co-generative systems. A complex demand scenario was used to test decision-making of the controllers. Experimental results were analyzed from both economic and reliability viewpoints. Abstract: In recent years, many different concepts to manage smart distributed systems were proposed and solutions developed. Smart grids and the increasing influence of renewable sources on energy production lead to concerns about grid stability and load balance. Combined Heat and Power (CHP) generators coupled with solar or other renewable sources offer the opportunity to satisfy both electric and thermal power economically. Both electric and thermal demand and supply change continuously, and sources such as solar and wind are not dispatchable or accurately predictable. At the same time, it is essential to use the most efficient and cost effective sources to satisfy the demand. This problem has been studied at the University of Genoa (UNIGE), Italy, using different generators and energy storage device that can supply both electric and thermal energy to consumer buildings. Here the problem is formulated as a constrained Multi-Input Multi-Output (MIMO) problem with sometimes conflicting requests that must be satisfied. The results come from experiments carried out on the test rig located at the Innovative Energy System Laboratories (IESL) of the Thermochemical Power Group (TPG) of UNIGE. This paper compares three different control approaches to manage the distributed generation system: Simplified Management Control (SMC), Model Predictive Control (MPC), and Multi-Commodity Matcher (MCM). Control systems and their control actions are evaluated through economic and performance key indicators.