Open Access

ARTICLE

Boundary Decision-Based Multi-Objective Robust Optimization for Microgrid Dispatching

Junjian Wu, Jingliao Sun^*, Yejun Xiang, Zhenyu Zhou, Zhengchai Shi

Electric Power Dispatching and Control Center, State Grid Wenzhou Electric Power Supply Company, Wenzhou, 325000, China

* Corresponding Author: Jingliao Sun. Email:

Energy Engineering 2026, 123(7), 18 https://doi.org/10.32604/ee.2025.073042

Received 09 September 2025; Accepted 03 December 2025; Issue published 18 June 2026

Abstract

The inherent unpredictability of renewable energy generation poses significant challenges to the reliable and economic dispatch of grid-connected microgrids. In response, this paper proposes a novel robust optimization strategy grounded in uncertain boundary decision-making and enhanced through innovations in the multi-objective cross-entropy method. An uncertainty budget-aware environmental economic dispatch model is first established, integrating photovoltaic and wind power generation. By employing mathematical sophistication—particularly Lagrangian transformation—the proposed method effectively resolves embedded uncertainties, transforming the original model into a deterministic multi-objective optimization framework robust against renewable energy volatility. Furthermore, by incorporating the dynamic operational demands of microgrids, this paper culminates in a robust optimization approach that is both fundamentally based on and adaptively responsive to uncertainty boundaries. To address the critical challenges of convergence and diversity in multi-objective optimization, crossover operators and an adaptive parameter update mechanism are introduced, significantly refining the conventional multi-objective cross-entropy algorithm. Case studies demonstrate the rationality and effectiveness of the proposed dispatch strategy and corroborate the superior performance and applicability of the enhanced algorithm.

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Keywords

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