TY - JOUR
T1 - An interactive tri-level multi-energy management strategy for heterogeneous multi-microgrids
AU - Cao, Yingping
AU - Zhou, Bin
AU - Or, Siu Wing
AU - Chan, Ka Wing
AU - Liu, Nian
AU - Zhang, Kuan
N1 - Funding Information:
This work was jointly supported by the Research Grants Council of the HKSAR Government (Grant No. R5020-18), the Innovation and Technology Commission of the HKSAR Government to the Hong Kong Branch of National Rail Transit Electrification and Automation Engineering Technology Research Center (Grant No. K-BBY1 ), the National Natural Science Foundation of China (Grant No. 51877072 ), and the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (Grant No. LAPSS20005 ).
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/10/15
Y1 - 2021/10/15
N2 - This paper proposes a multi-level multi-energy management framework for the coordinated and interactive operation of heterogeneous multi-microgrids (MMGs) based on many-criteria optimality. With the proposed framework, the highly nonlinear and complex MMG multi-energy management (MMGMEM) problem is formulated into tri-level scheduling subproblems with multi-energy couplings and multi-level interactions, in which the multi-energy trading with energy networks and multi-energy couplings within MGs are optimized in the upper and middle level, and a middle level is added to correct scheduling decisions of the upper level for coordinating the MMG multi-energy sharing. Then, a multi-step matrix decomposition technique is developed to decompose the high dimensional multi-energy coupling matrix of MMGs into the sum of three linear and sparse submatrices for improving the computation efficiency and scalability. Furthermore, a many-criteria decision making (MCDM) model is proposed for the multi-energy sharing problem to achieve an optimum tradeoff in which all microgrids (MGs) can benefit from electricity-gas exchanges, and an evolutionary many-objective optimization based on hyperplane transformation algorithm is used to solve the MCDM problem. Simulation results verify that the proposed framework can achieve a cost saving for each MG (over 19%), and validate its scalability in solving large-scale MMGMEM problems.
AB - This paper proposes a multi-level multi-energy management framework for the coordinated and interactive operation of heterogeneous multi-microgrids (MMGs) based on many-criteria optimality. With the proposed framework, the highly nonlinear and complex MMG multi-energy management (MMGMEM) problem is formulated into tri-level scheduling subproblems with multi-energy couplings and multi-level interactions, in which the multi-energy trading with energy networks and multi-energy couplings within MGs are optimized in the upper and middle level, and a middle level is added to correct scheduling decisions of the upper level for coordinating the MMG multi-energy sharing. Then, a multi-step matrix decomposition technique is developed to decompose the high dimensional multi-energy coupling matrix of MMGs into the sum of three linear and sparse submatrices for improving the computation efficiency and scalability. Furthermore, a many-criteria decision making (MCDM) model is proposed for the multi-energy sharing problem to achieve an optimum tradeoff in which all microgrids (MGs) can benefit from electricity-gas exchanges, and an evolutionary many-objective optimization based on hyperplane transformation algorithm is used to solve the MCDM problem. Simulation results verify that the proposed framework can achieve a cost saving for each MG (over 19%), and validate its scalability in solving large-scale MMGMEM problems.
KW - Many-criteria optimality
KW - Multi-energy management
KW - Multi-level scheduling
KW - Multi-microgrids
KW - Renewable energy
UR - http://www.scopus.com/inward/record.url?scp=85113398673&partnerID=8YFLogxK
U2 - 10.1016/j.jclepro.2021.128716
DO - 10.1016/j.jclepro.2021.128716
M3 - Journal article
AN - SCOPUS:85113398673
SN - 0959-6526
VL - 319
JO - Journal of Cleaner Production
JF - Journal of Cleaner Production
M1 - 128716
ER -