考虑 contingencies 的节点最优电力投资
Contingency-Aware Nodal Optimal Power Investments with High Temporal Resolution
AI总结：
本文提出 CANOPI 框架，解决考虑 contingencies 的节点电力投资问题，通过线性近似和固定点算法提升计算效率，展示在 1493 节点系统中的可靠性与经济性优势。
摘要：
我们提出了 CANOPI，一种新颖的算法框架，用于解决考虑 contingencies 的节点电力投资问题，这是一个大规模非线性优化问题，联合优化发电、储能和输电升级的投资，包括单位启停和长时储能的表示。由于输电升级对阻抗的影响，问题非线性；问题的规模来源于空间和时间分辨率的融合。我们提出了解决这些计算挑战的算法方法。我们对整体非线性模型提出线性近似，并开发了固定点算法以调整非线性阻抗反馈效应。我们使用专门的层次束方法求解大规模线性扩展模型，利用新颖的交错方法生成 contingency 约束生成。我们引入了最小环基算法，提高了基于环的直流功率流公式的数值稀疏性，加速了操作子问题的求解时间。CANOPI 在由真实地理网络数据构建的 1493 节点西联测试系统上得到验证，包含 52 周长场景的小时级操作和 200 亿个可能的传输 contingency 约束集。数值结果量化了在整合规划模型中纳入传输 contingencies 的可靠性和经济性效益，并突显了所提方法的计算优势。
英文摘要：
We present CANOPI, a novel algorithmic framework, for solving the Contingency-Aware Nodal Power Investments problem, a large-scale nonlinear optimization problem that jointly optimizes investments in generation, storage, and transmission upgrades, including representations of unit commitment and long-duration storage. The problem's scale arises from the confluence of spatial and temporal resolutions, along with the large number of contingency constraints. Further, the underlying problem is nonlinear due to transmission upgrades' impact on impedances. We propose algorithmic approaches to address these computational challenges. We pose a linear approximation and develop a fixed-point algorithm to adjust for nonlinear impedance feedback. We solve the large-scale linear expansion model with a specialized level-bundle method leveraging a novel interleaved approach to contingency constraint generation. We introduce a minimum cycle basis algorithm that improves numerical sparsity and solve times of cycle-based DC power flow. CANOPI is demonstrated on a 1493-bus Western Interconnection test system built from realistic-geography network data, with hourly operations spanning 52 week-long scenarios and a total possible set of 20 billion individual transmission contingency constraints. Numerical experiments quantify reliability and economic benefits of incorporating transmission contingencies in integrated planning models and highlight the computational advantages of the proposed methods.
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