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Robust energy management inactive distribution networks using mixed-integer convex optimization
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DOI：10.1016/j.epsr.2024.111367.png)
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Abstract
En 中文
Active distribution systems represent a significant evolution in power distribution infrastructure, characterized by their capability to integrate and manage a diverse array of distributed energy resources and advanced reactive power compensation technologies. This paper proposes a robust mixed-integer second-order cone model for the energy management problem in these active distribution systems. The proposed strategy takes advantage of various sources for reactive power management inactive distribution systems, including capacitor banks, photovoltaic systems, wind generators, and modern devices for continuous reactive power compensation, such as thyristor-switched capacitors and distribution static synchronous compensators. The objective of the proposed energy management approach is to minimize the total network generation costs through dynamic active and reactive power dispatch. This approach leverages the significant capacity of active distribution networks to regulate reactive power and efficiently coordinates the active power generation of distributed energy resources. The nonlinear exact model of the energy management problem is reformulated using a mixed-integer second-order cone relaxation, addressed through the branch-and-cut algorithm combined with the interior-point method via the YALMIP toolbox in MATLAB. The paper also addresses uncertainties in modern power systems by applying robust programming to the mixed-integer second-order cone model with YALMIP, accounting for unpredictable variations in power demand, and wind and solar energy generation, ensuring optimal scheduling for both reactive and active power dispatch in worst-case scenarios. The performance of the proposed methodology was double-checked using MATPOWER and SDP programming, demonstrating high accuracy with a maximum error of approximately 0.002%. The results show a reduction inactive energy losses by more than 35% and a 2% reduction in generation costs using the proposed EMP approach for the deterministic scenario compared to the base scenario. The results also demonstrate that the incorporation of uncertainties leads to a 13% increase in generation costs for the worst-case scenario.
Keywords:
Active distribution systems
Distributed energy resources
Energy management problem
Mixed-integer convex programming
Renewable energy integration
Robust optimization
Second-order cone programming
Uncertainties
Journal
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4.2
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