Distributionally Robust Chance-Constrained Energy Management of Microgrids Using Quantum Teaching Learning Based Optimization

Articles in Press

Document Type : Research paper

Authors

1 Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran.

2 Centre of Excellence in Power System Management and Control, Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran.

Abstract

Optimal microgrid (MG) energy management is a critical issue due to the significant expansion of renewable energy sources (RESs). However, unpredictable changes in demand and the inherent intermittency of RESs lead to considerable uncertainties. In this paper, the distributionally robust chance-constrained (DRCC) method is combined with the quantum version of the teaching–learning-based optimization (quantum TLBO) algorithm for the first time to address the energy management problem of a grid-connected MG. Specifically, the DRCC method is employed to model uncertainties in both loads and RESs. Then, the quantum TLBO (QTLBO) algorithm is utilized to solve the energy management problem with the objective of minimizing the operating cost of the MG. The simulation results clearly demonstrate that the QTLBO algorithm outperforms the TLBO, differential evolution (DE), and real-coded genetic algorithm (RCGA) in terms of both convergence speed to the final optimal solution and the quality of the solution achieved. Additionally, the effectiveness of the proposed DRCC method in handling uncertainties is compared with the robust optimization (RO) method. Numerical simulation results show that the DRCC method combined with QTLBO is 23.58% more effective than the RO method combined with QTLBO.

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Main Subjects

References

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Journal of Operation and Automation in Power Engineering

Articles in Press, Corrected Proof
Available Online from 18 July 2026

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History

  • Receive Date: 12 April 2025
  • Revise Date: 02 September 2025
  • Accept Date: 27 September 2025
  • First Publish Date: 18 July 2026

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