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<p>The integration of renewable energy sources into power grids presents a complex planning challenge characterized by hierarchical decision-making, significant uncertainties, and interdependent technical and economic constraints. Conventional planning models often fail to capture the multi-stakeholder interactions between regulators, grid operators, and renewable producers, leading to suboptimal and inefficient outcomes. This paper proposes a novel stochastic tri-level optimization framework to address the grid connection planning problem for renewable power plants under uncertainty. The model hierarchically integrates the decision processes of a regulatory authority (Level 1) minimizing societal costs, a grid operator (Level 2) minimizing infrastructure and operational costs, and renewable producers (Level 3) maximizing profits. It incorporates DC power flow constraints, transmission expansion, phased connection rollouts, and uncertainties in renewable generation, demand, and carbon prices. The resulting Mixed-Integer Nonlinear Programming (MINLP) problem is reformulated into a single-level equivalent using Karush-Kuhn-Tucker (KKT) conditions and MPEC techniques, and is solved via a hybrid algorithm combining Benders decomposition and Column-and-Constraint Generation (C&amp;CG). A case study on a modified IEEE 118-bus system demonstrates that the proposed framework yields a more robust and coordinated expansion plan compared to deterministic, bi-level, or centralized approaches. Results show that the stochastic tri-level model achieves higher renewable capacity (2018&#xa0;MW) with a lower total cost ($10,298&#xa0;M) and improved operational reliability, highlighting the critical value of coordinated policy and infrastructure planning under uncertainty for a sustainable energy transition.</p>

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Stochastic tri-level optimization for renewable power plant grid connection planning under uncertainty

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  • 出版模式：

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  • 发表日期：

    2026年7月22日

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  • Mohammadali Hormozi

摘要

The integration of renewable energy sources into power grids presents a complex planning challenge characterized by hierarchical decision-making, significant uncertainties, and interdependent technical and economic constraints. Conventional planning models often fail to capture the multi-stakeholder interactions between regulators, grid operators, and renewable producers, leading to suboptimal and inefficient outcomes. This paper proposes a novel stochastic tri-level optimization framework to address the grid connection planning problem for renewable power plants under uncertainty. The model hierarchically integrates the decision processes of a regulatory authority (Level 1) minimizing societal costs, a grid operator (Level 2) minimizing infrastructure and operational costs, and renewable producers (Level 3) maximizing profits. It incorporates DC power flow constraints, transmission expansion, phased connection rollouts, and uncertainties in renewable generation, demand, and carbon prices. The resulting Mixed-Integer Nonlinear Programming (MINLP) problem is reformulated into a single-level equivalent using Karush-Kuhn-Tucker (KKT) conditions and MPEC techniques, and is solved via a hybrid algorithm combining Benders decomposition and Column-and-Constraint Generation (C&CG). A case study on a modified IEEE 118-bus system demonstrates that the proposed framework yields a more robust and coordinated expansion plan compared to deterministic, bi-level, or centralized approaches. Results show that the stochastic tri-level model achieves higher renewable capacity (2018 MW) with a lower total cost ($10,298 M) and improved operational reliability, highlighting the critical value of coordinated policy and infrastructure planning under uncertainty for a sustainable energy transition.