A Review of Energy Management Systems and Organizational Structures of Prosumers
Nemanja Mišljenović,
Matej Žnidarec,
Goran Knežević,
Damir Šljivac,
Andreas Sumper
Affiliations
Nemanja Mišljenović
Department of Power Engineering, Faculty of Electrical Engineering, Computer Science and Information Technology, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
Matej Žnidarec
Department of Power Engineering, Faculty of Electrical Engineering, Computer Science and Information Technology, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
Goran Knežević
Department of Power Engineering, Faculty of Electrical Engineering, Computer Science and Information Technology, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
Damir Šljivac
Department of Power Engineering, Faculty of Electrical Engineering, Computer Science and Information Technology, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
Andreas Sumper
Centre d’Innovació Tecnològica en Convertidors Estàtics i Accionaments (CITCEA-UPC), Departament d’Enginyeria Elèctrica, Universitat Politècnica de Catalunya. ETS d’Enginyeria Industrial de Barcelona, Av. Diagonal, 647, Pav. G, 23.25, 08028 Barcelona, Spain
This review provides the state of the art of energy management systems (EMS) and organizational structures of prosumers. Integration of renewable energy sources (RES) into the household brings new challenges in optimal operation, power quality, participation in the electricity market and power system stability. A common solution to these challenges is to develop an EMS with different prosumer organizational structures. EMS development is a multidisciplinary process that needs to involve several aspects of observation. This paper provides an overview of the prosumer organizational and control structures, types and elements, prediction methods of input parameters, optimization frameworks, optimization methods, objective functions, constraints and the market environment. Special attention is given to the optimization framework and prediction of input parameters, which represents room for improvement, that mitigate the impact of uncertainties associated with RES-based generation, consumption and market prices on optimal operation.
