High-Spatial Resolution Transmission and Storage Expansion Planning for High Renewable Grids: A Case Study

TL;DR

This paper addresses co-optimization of transmission expansion and short-duration storage in a high-renewable grid context. It introduces a two-stage TEP+Storage model and a Storage Candidates (SC) heuristic to tractably solve a 2000-bus Texas grid case, using five representative days. Results show transmission investments lead renewable utilization, while storage enables peak shaving and load balancing, with co-optimization delivering lower costs and improved reliability than either technology alone. Limitations include potential SC-missed locations and the need for higher temporal resolution and advanced decomposition techniques; future work targets iterative SC expansion and dynamic candidate selection to further enhance scalability and performance.

Abstract

Transmission Expansion Planning (TEP) is the process of optimizing the development and upgrade of the power grid to ensure reliable, efficient, and cost-effective electricity delivery while addressing grid constraints. To support growing demand and renewable energy integration, energy storage is emerging as a pivotal asset that provides temporal flexibility and alleviates congestion. This paper presents a TEP model that incorporates the sizing and siting of short-duration storage. With a focus on high spatial resolution, the model is applied to a 2,000-bus synthetic Texas power system, offering detailed insights into geographic investment and operational patterns. To maintain computational feasibility, a simple yet effective storage candidates (SC) method is introduced, significantly reducing the search space. Results highlight that transmission investments are primarily driven by renewable energy expansion, while storage investments are shaped by renewable curtailment and load-shedding events, with their primary function being peak load shaving. The findings underscore the importance of co-optimizing transmission and storage to minimize costs and enhance grid reliability. However, limitations in the ability of the SC method to identify optimal storage locations to meet long-term needs suggest opportunities for future research, including dynamic candidate selection and hybrid optimization techniques.

Figures (10)

• Figure 1: Search Space Reduction using the Proposed SC Method.
• Figure 2: Topology of Texas grid, with notable load centers labelled. Blue lines indicate branches, and red nodes indicate busses.
• Figure 3: Solar capacity of the system. Circles indicate the location of generators, with color and size corresponding to capacity in MW.
• Figure 4: Wind capacity of the system. Circles indicate the location of generators, with color and size corresponding to capacity in MW.
• Figure 5: Nonrenewable capacity of the system. Circles indicate the location of generators, with color and size corresponding to capacity in MW.