TY - JOUR
T1 - Multi-Stage Adaptive Stochastic-Robust Scheduling Method with Affine Decision Policies for Hydrogen-Based Multi-Energy Microgrid
AU - Zhou, Yuzhou
AU - Zhai, Qiaozhu
AU - Xu, Zhanbo
AU - Wu, Lei
AU - Guan, Xiaohong
N1 - Publisher Copyright:
© 2010-2012 IEEE.
PY - 2024/5/1
Y1 - 2024/5/1
N2 - Zero-carbon clean energy such as hydrogen has been developed rapidly to reduce carbon emissions, gradually promoting them as the main energy supply for multi-energy microgrids (MEMGs), which motivates the deployment of hydrogen-based MEMG (H-MEMG). The main difficulty of the H-MEMG scheduling problem is how to handle source/load uncertainties for ensuring the solution feasibility and economics in the actual operation. To this end, this paper proposes a novel multi-stage adaptive stochastic-robust optimization (MASRO) approach, which combines the ideas of stochastic programming and multi-stage robust optimization. The established model has the objective of the expected operation cost and ensures the solution feasibility by designed constraints rather than the 'min-max' structure. Specifically, first, affine policies are adapted to describe the complex relationship between decision variables and uncertainty realizations; Second, an affine policy-based solution approach is proposed for the MASRO H-MEMG scheduling model. Then, the complex conversion relationship and coupling constraints are reformulated, and a tractable mixed-integer linear programming (MILP) model is established; Third, based on the solved affine functions, the real-Time rolling and non-rolling economic dispatch models are proposed to respectively pursue the economic and computational requirements, and both can guarantee solution robustness and nonanticipativity. Numerical tests are implemented on a real H-MEMG, verifying that the proposed method could guarantee the feasibility and economic efficiency of actual H-MEMG operations.
AB - Zero-carbon clean energy such as hydrogen has been developed rapidly to reduce carbon emissions, gradually promoting them as the main energy supply for multi-energy microgrids (MEMGs), which motivates the deployment of hydrogen-based MEMG (H-MEMG). The main difficulty of the H-MEMG scheduling problem is how to handle source/load uncertainties for ensuring the solution feasibility and economics in the actual operation. To this end, this paper proposes a novel multi-stage adaptive stochastic-robust optimization (MASRO) approach, which combines the ideas of stochastic programming and multi-stage robust optimization. The established model has the objective of the expected operation cost and ensures the solution feasibility by designed constraints rather than the 'min-max' structure. Specifically, first, affine policies are adapted to describe the complex relationship between decision variables and uncertainty realizations; Second, an affine policy-based solution approach is proposed for the MASRO H-MEMG scheduling model. Then, the complex conversion relationship and coupling constraints are reformulated, and a tractable mixed-integer linear programming (MILP) model is established; Third, based on the solved affine functions, the real-Time rolling and non-rolling economic dispatch models are proposed to respectively pursue the economic and computational requirements, and both can guarantee solution robustness and nonanticipativity. Numerical tests are implemented on a real H-MEMG, verifying that the proposed method could guarantee the feasibility and economic efficiency of actual H-MEMG operations.
KW - hydrogen
KW - Multi-energy microgrid
KW - nonanticipativity
KW - robustness
KW - stochastic-robust scheduling
UR - http://www.scopus.com/inward/record.url?scp=85180283217&partnerID=8YFLogxK
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U2 - 10.1109/TSG.2023.3340727
DO - 10.1109/TSG.2023.3340727
M3 - Article
AN - SCOPUS:85180283217
SN - 1949-3053
VL - 15
SP - 2738
EP - 2750
JO - IEEE Transactions on Smart Grid
JF - IEEE Transactions on Smart Grid
IS - 3
ER -