Minimize Total System Costs with Co-Optimization

Why Co-Optimization Matters Now

The structural assumptions that once separated generation planning from transmission planning are no longer holding. The assumption that generation is available whenever it is needed is becoming less valid as systems integrate greater levels of intermittent resources.

Electrification is accelerating. Customer adoption of electric technologies is increasing load across buildings, industry, fleets, and transportation. The result is higher peak demand, more dynamic load shapes, and geographically concentrated growth.

The supply mix is changing just as rapidly. Generation portfolios are shifting toward solar, wind, storage, and distributed energy resources (DERs) resulting in more dynamic generation availability. Integrating them reliably requires tighter coordination between generation siting, transmission upgrades, and system operations.

New classes of large loads are emerging. Advanced computing infrastructure, such as artificial intelligence (AI) data centers, can exceed 1,000 MW and often seek service on compressed timelines. Their load profiles are uncertain and can materially alter regional transmission needs.

Policy timelines are compressing investment windows. Recent U.S. federal incentive changes accelerated tax credit phaseouts, which narrows the qualification timelines for new wind and solar projects. With hundreds of gigawatts in interconnection queues nationwide, generation decisions are being made under compressed economic timelines, while transmission expansion remains capital-intensive, study-driven, and slow to deliver.

In this environment, the current practice of isolated planning for generation and transmission planning (G&T) is economically exposed.

• Key Point: The more critical design choice is co-optimization to better understand the full picture. Jointly evaluating G&T in expansion models, has become essential. It allows utilities and planners to improve economic efficiency, reliability, and the practical realities of a rapidly transforming grid. An emerging, leading industry practice involves co-optimization of G&T in expansion modeling as necessary to support more integrated system planning and is increasingly becoming the gold standard to meet regulations like FERC Order 1920.

A Better Approach to Minimize Total System Costs

Capacity Expansion Models (CEMs) are the backbone of long-term power system planning, guiding billions of dollars in investment decisions over 15 to 20-year horizons. Looking ahead, the latest NERC Long-Term Reliability Assessment report estimates that the United States will require up to 41,000 miles of new transmission lines in the next 10 years to maintain system reliability.¹ On the generation side, the EIA Annual Energy Outlook 2025 suggests up to 4-6,000TWh of new generation could be needed between now and 2050 to serve the rapidly increasing electricity demand.² These estimates likely included some form of CEM during the analysis process, informing billions of dollars of investment.

When generation and transmission are optimized independently, the implicit assumption is that one can be held constant while the other is decided.

• Key Point: But this sequential or “siloed” approach introduces inefficiencies. For example:

Further Complications in CEM

Zonal vs. Nodal Representation: Pragmatism Meets Precision

The spatial granularity of the network model is a defining tension in long-term Capacity Expansion Model design. Nodal models represent every bus, line, and contingency in the transmission network, yielding the most physically accurate power flow representation. However, the combinatorial explosion of investment candidates across thousands of nodes and dozens of time periods renders full nodal co-optimization computationally intractable over 15 to 20-year horizons. 

• Key Point: Zonal models, by contrast, aggregate the network into manageable regions connected by transfer limits—sacrificing intra-zonal precision but making co-optimization practical. A promising middle ground is a “reduced network” approach, which simplifies the nodal topology while preserving key electrical characteristics.

Co-Optimization Provides Sharper Investment Signals

As the grid undergoes rapid structural change, jointly planning generation and transmission is no longer a modeling preference but a strategic imperative. Independent planning approaches routinely lock in inefficiencies such as siting low-cost renewables far from load without accounting for delivery costs, or reinforcing transmission paths that become misaligned with future supply patterns. Co-optimization corrects these distortions by internalizing systemwide tradeoffs and identifying investments that minimize total, rather than siloed, costs over multidecade horizons.

Co-optimization of G&T necessarily requires greater topological granularity to ensure that important transmission paths are being modelled. Planning frameworks that co-optimization with zonal or reduced-network spatial approaches strike the right balance. The objective is to maintain computational feasibility while preserving the operational realities of a variable, weather driven grid. Utilities should ensure that planning models include sufficient topological granularity to capture critical power transfer corridors so that co-optimization can deliver results more robust than analyzing generation and transmission separately.

For decision-makers, the takeaway is clear. Co-optimization:

✓ Provides sharper investment signals, with comprehensive models that take endogenously includes the trade-off between generation and transmission.

✓ Reduces the risk of costly over or underbuilds, especially when executed by a team that has experience in these areas.

✓ Supports a more reliable and economically efficient expansion of critical power systems.

Adopting this integrated approach ensures that long-term infrastructure choices remain aligned with both system needs and the evolving dynamics of the energy transition.

1. NERC – Long Term Reliability Assessment.

2. Annual Energy Outlook 2025 - U.S. Energy Information Administration (EIA).

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