Title :
Decentralized Control of the Power Flows in a Network of Smart Microgrids Modeled as a Team of Cooperative Agents
Author :
Dagdougui, Hanane ; Sacile, Roberto
Author_Institution :
Fac. of Eng., Univ. of Genova, Genoa, Italy
Abstract :
The focus of this paper is on the decentralized control of smart microgrids (SMGs), where each microgrid is modeled as an inventory system locally producing energy by wind/solar sources. The objective is to satisfy the internal demand, and to exchange power with its local energy storage technology, the main grid, and other similar microgrids of the region. The problem faced, within this context, is the optimization (minimization) of the costs of energy storage and power exchanged among SMGs. A decentralized control strategy is proposed, which allows the storage level in each microgrid to operate around a reference value by cooperatively sharing power between microgrids. A distributed control approach is presented where different agents, one for each microgrid and one for each power line, agree on a saddle point of a local function. The approach is based on the classical work of Arrow on convex optimization, which has seen renewed interest with its recent application to team theory and in its connection with the decomposition of feedback systems. An example is illustrated to show the practical use and the limitations of the method.
Keywords :
convex programming; cooperative systems; decentralised control; distributed control; distributed power generation; energy storage; load flow control; multi-agent systems; power cables; power generation control; SMGs; convex optimization; cooperative agent team; decentralized control strategy; distributed control approach; energy storage cost optimization; feedback system decomposition; inventory system; local energy storage technology; local function; multiagent systems; power flow control; power line; saddle point; smart microgrids; team theory; wind-solar sources; Active power distribution; convex optimization; decentralized control; dual decomposition; linear quadratic tracking control; multi agent systems (MAS); optimal power flow; saddle point solution method; smart power applications; team theory;
Journal_Title :
Control Systems Technology, IEEE Transactions on
DOI :
10.1109/TCST.2013.2261071