TY - JOUR
T1 - Multi-Stage Real-Time Operation of a Multi-Energy Microgrid with Electrical and Thermal Energy Storage Assets
T2 - A Data-Driven MPC-ADP Approach
AU - Li, Zhengmao
AU - Wu, Lei
AU - Xu, Yan
AU - Moazeni, Somayeh
AU - Tang, Zao
N1 - Publisher Copyright:
© 2010-2012 IEEE.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - This paper studies the multi-stage real-time stochastic operation of grid-tied multi-energy microgrids (MEMGs) via the hybrid model predictive control (MPC) and approximate dynamic programming (ADP) approach. In the MEMG, practical power and thermal network constraints, heterogeneous energy storage devices, and distributed generations are involved. Given the relatively large thermal inertia and slow thermal energy fluctuation, only uncertainties of renewable energy sources and active/reactive power loads are considered. Then, historical data are adopted as training scenarios for the MPC-ADP method to acquire empirical knowledge for dealing with all the diverse uncertainties. Further, piecewise linear functions are used to approximate value functions with respect to the operation status of energy storage assets, which enables sequentially solving the Bellman's equation forward through time to minimize MEMG operation cost. Finally, numerical case studies are conducted to illustrate the effectiveness and superiority of the proposed MPC-ADP approach. Simulation results indicate that with sufficient information embedded, the MPC-ADP approach could obtain good-enough real-time operation solutions with the successively updated forecast. Further, it outperforms alternative real-time operation benchmarks in terms of optimality and convergence for various application scenarios.
AB - This paper studies the multi-stage real-time stochastic operation of grid-tied multi-energy microgrids (MEMGs) via the hybrid model predictive control (MPC) and approximate dynamic programming (ADP) approach. In the MEMG, practical power and thermal network constraints, heterogeneous energy storage devices, and distributed generations are involved. Given the relatively large thermal inertia and slow thermal energy fluctuation, only uncertainties of renewable energy sources and active/reactive power loads are considered. Then, historical data are adopted as training scenarios for the MPC-ADP method to acquire empirical knowledge for dealing with all the diverse uncertainties. Further, piecewise linear functions are used to approximate value functions with respect to the operation status of energy storage assets, which enables sequentially solving the Bellman's equation forward through time to minimize MEMG operation cost. Finally, numerical case studies are conducted to illustrate the effectiveness and superiority of the proposed MPC-ADP approach. Simulation results indicate that with sufficient information embedded, the MPC-ADP approach could obtain good-enough real-time operation solutions with the successively updated forecast. Further, it outperforms alternative real-time operation benchmarks in terms of optimality and convergence for various application scenarios.
KW - Hybrid model predictive control-approximate dynamic programming
KW - heterogeneous energy storage
KW - multi-energy microgrid
KW - stochastic operation
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U2 - 10.1109/TSG.2021.3119972
DO - 10.1109/TSG.2021.3119972
M3 - Article
AN - SCOPUS:85117267323
SN - 1949-3053
VL - 13
SP - 213
EP - 226
JO - IEEE Transactions on Smart Grid
JF - IEEE Transactions on Smart Grid
IS - 1
ER -