Renewable energy sources like wind and solar have quickly changed modern power systems into uncertain, nonlinear, and dynamically linked environments. Traditional deterministic Optimal Power Flow (OPF) methods do not account for stochastic variability, highlighting the need for a probabilistic and robust framework. This study introduces a stochastic Renewable-Integrated Multi-Objective Optimal Power Flow (MOOPF–RE) model that minimizes total generation cost, emissions, active power loss, voltage deviation, and voltage instability amid renewable uncertainty. Wind and solar power outputs use Weibull and lognormal probability density functions. A hybrid MOABC–NSGA-II metaheuristic combines the exploration strength of the ABC algorithm with the elitist sorting and diversity preservation of NSGA-II. Hybridization is strengthened by two main co-operateive mechanisms as Diversity-Maintenance <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$\epsilon$</EquationSource> <EquationSource Format="MATHML"><math> <mi>ϵ</mi> </math></EquationSource> </InlineEquation>-constraint (DM–<InlineEquation ID="IEq2"> <EquationSource Format="TEX">$\epsilon$</EquationSource> <EquationSource Format="MATHML"><math> <mi>ϵ</mi> </math></EquationSource> </InlineEquation>) strategy for effective constraint handling and <InlineEquation ID="IEq3"> <EquationSource Format="TEX">$\epsilon$</EquationSource> <EquationSource Format="MATHML"><math> <mi>ϵ</mi> </math></EquationSource> </InlineEquation>-Dominance-Crowding Distance (ED–CD) archiving method for balancing convergence and diversity. A combined AHP-TOPSIS multi-criteria decision-making layer is used to find the Best Compromise Solution (BCS), providing clarity for power system operators. Simulation studies on IEEE 30- and 57-bus systems under bi-, tri-, and quad-objective conditions confirm the model’s robustness. In Case 5, the hybrid algorithm optimizes cost, emission, and voltage deviation, achieving a 7.3% lower cost and 6.8% reduced emission compared to NSGA-II, along with improved Pareto spread and constraint feasibility. In Case 10 (quad-objective scenario with voltage stability), the proposed method shows better diversity and a 9.1% improvement in convergence metric compared to MOABC, while keeping runtime efficiency nearly the same. Wilcoxon tests confirm significant performance improvements in all metrics. The proposed framework offers a scalable, sustainable, and techno-economic efficient for resilient, Net-zero grid operation.

Meta-Strategy Epsilon-Dominance Co-operative Mechanism for Renewable-Integrated Multi-objective Optimal Power Flow Using Hybrid Artificial Bee Colony and NSGA-II Algorithm

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2025年11月24日

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Abhishek Bajirao Katkar,
Himmat Tukaram Jadhav

摘要

Renewable energy sources like wind and solar have quickly changed modern power systems into uncertain, nonlinear, and dynamically linked environments. Traditional deterministic Optimal Power Flow (OPF) methods do not account for stochastic variability, highlighting the need for a probabilistic and robust framework. This study introduces a stochastic Renewable-Integrated Multi-Objective Optimal Power Flow (MOOPF–RE) model that minimizes total generation cost, emissions, active power loss, voltage deviation, and voltage instability amid renewable uncertainty. Wind and solar power outputs use Weibull and lognormal probability density functions. A hybrid MOABC–NSGA-II metaheuristic combines the exploration strength of the ABC algorithm with the elitist sorting and diversity preservation of NSGA-II. Hybridization is strengthened by two main co-operateive mechanisms as Diversity-Maintenance $\epsilon$ $ϵ$ -constraint (DM– $\epsilon$ $ϵ$ ) strategy for effective constraint handling and $\epsilon$ $ϵ$ -Dominance-Crowding Distance (ED–CD) archiving method for balancing convergence and diversity. A combined AHP-TOPSIS multi-criteria decision-making layer is used to find the Best Compromise Solution (BCS), providing clarity for power system operators. Simulation studies on IEEE 30- and 57-bus systems under bi-, tri-, and quad-objective conditions confirm the model’s robustness. In Case 5, the hybrid algorithm optimizes cost, emission, and voltage deviation, achieving a 7.3% lower cost and 6.8% reduced emission compared to NSGA-II, along with improved Pareto spread and constraint feasibility. In Case 10 (quad-objective scenario with voltage stability), the proposed method shows better diversity and a 9.1% improvement in convergence metric compared to MOABC, while keeping runtime efficiency nearly the same. Wilcoxon tests confirm significant performance improvements in all metrics. The proposed framework offers a scalable, sustainable, and techno-economic efficient for resilient, Net-zero grid operation.