Efficient Energy-Management System Using A Hybrid Transactive-Model Predictive Control Mechanism for Prosumer-Centric Networked Microgrids

Eric Galvan, Paras Mandal, Shantanu Chakraborty and Tomonobu Senjyu
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Eric Galvan: Power and Renewable Energy Systems (PRES) Lab, Department of Electrical and Computer Engineering, University of Texas at El Paso, El Paso, TX 79968, USA
Paras Mandal: Power and Renewable Energy Systems (PRES) Lab, Department of Electrical and Computer Engineering, University of Texas at El Paso, El Paso, TX 79968, USA
Shantanu Chakraborty: Energy Transition Hub, University of Melbourne, Carlton, Victoria 3053, Australia
Tomonobu Senjyu: Faculty of Engineering, University of the Ryukyus, 1 Senbaru, Nishihara-cho, Nakagami, Okinawa 903-0213, Japan

Sustainability, 2019, vol. 11, issue 19, 1-24

Abstract: With the development of distributed energy resources (DERs) and advancements in technology, microgrids (MGs) appear primed to become an even more integral part of the future distribution grid. In order to transition to the smart grid of the future, MGs must be properly managed and controlled. This paper proposes a microgrid energy management system (MGEMS) based on a hybrid control algorithm that combines Transactive Control (TC) and Model Predictive Control (MPC) for an efficient management of DERs in prosumer-centric networked MGs. A locally installed home energy management system (HEMS) determines a charge schedule for the battery electric vehicle (BEV) and a charge–discharge schedule for the solar photovoltaic (PV) and battery energy storage system (BESS) to reduce residential customers’ operation cost and to improve their overall savings. The proposed networked MGEMS strategy was implemented in IEEE 33-bus test system and evaluated under different BEV and PV-BESS penetration scenarios to study the potential impact that large amounts of BEV and PV-BESS systems can have on the distribution system and how different pricing mechanisms can mitigate these impacts. Test results indicate that our proposed MGEMS strategy shows potential to reduce peak load and power losses as well as to enhance customers’ savings.

Keywords: battery energy storage; electric vehicle; microgrid; model predictive control; monte carlo simulation; transactive control (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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