Non-Iterative Coordination of Interconnected Power Grids via Dimension-Decomposition-Based Flexibility Aggregation

TL;DR

This work tackles the challenge of coordinating interconnected regional power grids under privacy and high-dimensional constraints. It introduces a dimension-decomposition-based inner-approximation method to compute per-RPG aggregated flexibility regions by projecting high-dimensional feasibility onto boundary variables and recombining subspaces, enabling non-iterative tie-line scheduling. The approach yields a physically interpretable counterpart to an equivalent generator network and derives an aggregated cost function κ_t(p^tie_t) via sampling and convex piecewise-linear fitting, validated on a five-region European grid. Compared with traditional methods, the method significantly reduces conservatism and demonstrates scalability for large-scale systems, while providing useful initial values for subsequent iterative coordination. The results suggest a practical pathway to privacy-preserving, scalable, non-iterative coordination in modern interconnected power systems.

Abstract

The bulk power grid is divided into regional grids interconnected with multiple tie-lines for efficient operation. Since interconnected power grids are operated by different control centers, it is a challenging task to realize coordinated dispatch of multiple regional grids. A viable solution is to compute a flexibility aggregation model for each regional power grid, then optimize the tie-line schedule using the aggregated models to implement non-iterative coordinated dispatch. However, challenges such as intricate interdependencies and curse of dimensionality persist in computing the aggregated models in high-dimensional space. Existing methods like Fourier-Motzkin elimination, vertex search, and multi-parameter programming are limited by dimensionality and conservatism, hindering practical application. This paper presents a novel dimension-decomposition-based flexibility aggregation algorithm for calculating the aggregated models of multiple regional power grids, enabling non-iterative coordination in large-scale interconnected systems. Compared to existing methods, the proposed approach yields a significantly less conservative flexibility region. The derived flexibility aggregation model for each regional power grid has a well-defined physical counterpart, which facilitates intuitive analysis of multi-port regional power grids and provides valuable insights into their internal resource endowments. Numerical tests validate the feasibility of the aggregated model and demonstrate its accuracy in coordinating interconnected power grids.

Figures (16)

• Figure 1: Aggregation of regional power grids for coordinated dispatch of interconnected systems.
• Figure 2: Physical and mathematical perspectives of system flexibility aggregation.
• Figure 3: Schematic diagram of the inner-approximate the projected flexibility region.
• Figure 4: Schematic diagram of the polytope-bound shrinking algorithm: (a) Calculate the circumscribed polytope $\hat{\mathcal{R}}_{\text{L}(0)}$ at the beginning of iteration; (b) Identify the outlier point $\bm{p}^\text{out}_{(k)}$ of the shrinking polytope $\hat{\mathcal{R}}_{\text{L}(k)}$ in the current $k$-th iteration. The nearest point on the boundary of the projected polytope $\mathcal{R}_\text{L}$ is denoted as $\bm{p}^\text{bnd}_{(k)}$; (c) Shrink the boundaries to draw back the extreme point $\bm{p}^\text{out}_{(k)}$ to the boundary point $\bm{p}^\text{bnd}_{(k)}$ by adjusting parameters $\bm{b}_{(k)}$; (d) Repeat the bound shrinking process to finally obtain the inner-approximated polytope $\hat{\mathcal{R}}_\text{L} \subseteq \mathcal{R}_\text{L}$.
• Figure 5: Schematic diagram of the IEEE 118 regional power grid.