TY - GEN
T1 - A coordinated model predictive control of grid-connected energy storage systems
AU - Abdelghany, Muhammad Bakr
AU - Al-Durra, Ahmed
N1 - Publisher Copyright:
© 2023 American Automatic Control Council.
PY - 2023
Y1 - 2023
N2 - The environmental goals set out in the 2015 Paris Agreement on climate change lead to the design and the definition of energy management strategies based on renewable energy sources (RESs). In this regard, the integration of energy storage systems (ESSs) into the microgrid requires the development of sophisticated control systems for their management and the reduction of their degradation. Moreover, external agents, e.g., battery/fuel cell electric vehicles, exchange energy with microgrids using electric and hydrogen markets. This paper presents a novel energy management strategy to control a microgrid which includes RESs paired with a battery-ESS and a hydrogenESS, and consumer loads. The strategy, based on the model predictive control (MPC) framework, takes into account ESSs' economical and operating costs, degradation issues, and physical and dynamical system constraints. Numerical simulations show the effectiveness of the strategy, which successfully manages the plant by fulfilling constraints and energy requests while reducing device costs and increasing battery life.
AB - The environmental goals set out in the 2015 Paris Agreement on climate change lead to the design and the definition of energy management strategies based on renewable energy sources (RESs). In this regard, the integration of energy storage systems (ESSs) into the microgrid requires the development of sophisticated control systems for their management and the reduction of their degradation. Moreover, external agents, e.g., battery/fuel cell electric vehicles, exchange energy with microgrids using electric and hydrogen markets. This paper presents a novel energy management strategy to control a microgrid which includes RESs paired with a battery-ESS and a hydrogenESS, and consumer loads. The strategy, based on the model predictive control (MPC) framework, takes into account ESSs' economical and operating costs, degradation issues, and physical and dynamical system constraints. Numerical simulations show the effectiveness of the strategy, which successfully manages the plant by fulfilling constraints and energy requests while reducing device costs and increasing battery life.
UR - http://www.scopus.com/inward/record.url?scp=85164106721&partnerID=8YFLogxK
U2 - 10.23919/ACC55779.2023.10155903
DO - 10.23919/ACC55779.2023.10155903
M3 - Conference contribution
AN - SCOPUS:85164106721
T3 - Proceedings of the American Control Conference
SP - 1862
EP - 1867
BT - 2023 American Control Conference, ACC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 American Control Conference, ACC 2023
Y2 - 31 May 2023 through 2 June 2023
ER -