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<p>The high use of renewable energy sources creates significant uncertainty, nonlinearity, and operational risk in modern power systems, making traditional deterministic and stochastic optimal power flow models insufficient. This paper introduces a distributionally robust chance-constrained probabilistic multi-objective optimal power flow (DRCC-PMOOPF) framework for risk-aware and secure operation amid wind, solar, and load uncertainty. The proposed model differs from earlier state-of-the-art methods—like CCMO, ToP, NSGA-II, ST-IWO, MOEA/D, MOABC, and MPIO-COSR—that depend on fixed distributional assumptions or weak feasibility restoration. It explicitly addresses distributional ambiguity using chance constraints at set confidence levels. A novel hybrid Modified Artificial Bee Colony-NSGA-II (MOABC-NSGA-II) algorithm is developed to tackle the large-scale, highly non-convex optimization problem. The proposed optimizer combines the global exploration of ABC, elitist non-dominated sorting from NSGA-II, and a sophisticated SF-based constraint-handling mechanism, ensuring strong feasibility and well-distributed Pareto fronts. A TOPSIS-based decision-support module is included to objectively identify an implementable Best Compromise Solution, improving the practical relevance of the Pareto sets. Thorough evaluations on IEEE 30-, 57-, and 118-bus systems across bi-, tri-, and multiple-objective scenarios show that the proposed framework consistently excels. The proposed approach reduces IGD by about 15–40% and improves hypervolume by 10–25%, showing better convergence and diversity. Operationally, average computational time decreases by 6–7% compared to MOABC and over 40% compared to NSGA-II, indicating reduced computational burden and better scalability. The Wilcoxon rank-sum test confirms that the observed improvements are statistically significant. The results showcase the innovation, strength, and efficiency of the DRCC-PMOOPF framework as a leading solution for the secure and sustainable operation of renewable-rich power systems. </p>

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A Distributionally Robust Chance-constrained Multi-objective OPF Framework For Renewable-rich Power Systems based on hybrid MOABC-NSGA-II Optimization

  • 期刊论文

  • 发表日期：

    2026年4月10日

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  • Abhishek Bajirao Katkar,
  • Himmat Tukaram Jadhav

摘要

The high use of renewable energy sources creates significant uncertainty, nonlinearity, and operational risk in modern power systems, making traditional deterministic and stochastic optimal power flow models insufficient. This paper introduces a distributionally robust chance-constrained probabilistic multi-objective optimal power flow (DRCC-PMOOPF) framework for risk-aware and secure operation amid wind, solar, and load uncertainty. The proposed model differs from earlier state-of-the-art methods—like CCMO, ToP, NSGA-II, ST-IWO, MOEA/D, MOABC, and MPIO-COSR—that depend on fixed distributional assumptions or weak feasibility restoration. It explicitly addresses distributional ambiguity using chance constraints at set confidence levels. A novel hybrid Modified Artificial Bee Colony-NSGA-II (MOABC-NSGA-II) algorithm is developed to tackle the large-scale, highly non-convex optimization problem. The proposed optimizer combines the global exploration of ABC, elitist non-dominated sorting from NSGA-II, and a sophisticated SF-based constraint-handling mechanism, ensuring strong feasibility and well-distributed Pareto fronts. A TOPSIS-based decision-support module is included to objectively identify an implementable Best Compromise Solution, improving the practical relevance of the Pareto sets. Thorough evaluations on IEEE 30-, 57-, and 118-bus systems across bi-, tri-, and multiple-objective scenarios show that the proposed framework consistently excels. The proposed approach reduces IGD by about 15–40% and improves hypervolume by 10–25%, showing better convergence and diversity. Operationally, average computational time decreases by 6–7% compared to MOABC and over 40% compared to NSGA-II, indicating reduced computational burden and better scalability. The Wilcoxon rank-sum test confirms that the observed improvements are statistically significant. The results showcase the innovation, strength, and efficiency of the DRCC-PMOOPF framework as a leading solution for the secure and sustainable operation of renewable-rich power systems.