Multi-Objective Transmission Expansion: An Offshore Wind Power Integration Case Study

TL;DR

The paper tackles the lack of coordinated offshore grid-planning guidance by introducing a multi-objective, multi-stage GS&TEP model that explicitly internalizes negative externalities from power generation. It couples generation, storage, and transmission expansion, assesses offshore wind POIs, and analyzes extreme operational scenarios using ISO-NE and PJM case studies. Key findings show that incorporating externalities shifts investment toward clean generation and storage, can reduce onshore line upgrades, and that optimizing POIs and considering extreme days materially affect topology and costs. The framework provides regulators with a tool to balance economic and societal objectives in offshore wind integration and related transmission planning.

Abstract

Despite ambitious offshore wind targets in the U.S. and globally, offshore grid planning guidance remains notably scarce, contrasting with well-established frameworks for onshore grids. This gap, alongside the increasing penetration of offshore wind and other clean-energy resources in onshore grids, highlights the urgent need for a coordinated planning framework. Our paper describes a multi-objective, multistage generation, storage and transmission expansion planning model to facilitate efficient and resilient large-scale adoption of offshore wind power. Recognizing regulatory emphasis and, in some cases, requirements to consider externalities, this model explicitly accounts for negative externalities: greenhouse gas emissions and local emission-induced air pollution. Utilizing an 8-zone ISO-NE test system and a 9-zone PJM test system, we explore grid expansion sensitivities such as impacts of optimizing Points of Interconnection (POIs) versus fixed POIs, negative externalities, and consideration of extreme operational scenarios resulting from offshore wind integration. Our results indicate that accounting for negative externalities necessitates greater upfront investment in clean generation and storage (balanced by lower expected operational costs). Optimizing POIs could significantly reshape offshore topology or POIs, and lower total cost. Finally, accounting for extreme operational scenarios typically results in greater operational costs and sometimes may alter onshore line investment.

Figures (26)

• Figure 1: Stages for Expansion/Investment Decisions and Ensuing Operations. (Notes: Each expansion decision stage and ensuing operations can be subject to long- and short-term uncertainty.)
• Figure 2: Hourly Normalized Net Load for Normal and Extreme Scenarios.
• Figure 3: Average Marginal Damages from Local Air Pollution in ISO-NE. (Notes: Size of the red dots represents the $/MWh average marginal damages with a maximum value of 535.75 $/MWh and a minimum value of 0.31 $/MWh.)
• Figure 4: Optimal Onshore and Offshore Topology: Impacts of Accounting for Externalities and Extreme Days. (Notes: The line between NEMA and SEMA in SO is upgraded in Epoch 3 whereas in SO X5 in Epoch 1, the default epoch for transmission buildouts if not mentioned explicitly.)
• Figure 5: Optimal Onshore and Offshore Topology: Impacts of Accounting for Externalities and Extreme Days Using Operational Scenarios Derived from scott2019clustering. (Notes: The line between NEMA and RI in SO is upgraded in Epoch 2, whereas in SO X5 and MO in Epoch 1.)