Optimizing Deep Decarbonization Pathways in California with Power System Planning Using Surrogate Level-based Lagrangian Relaxation

TL;DR

The paper tackles California's 2045 net-zero goal by formulating a detailed MILP for long-horizon power-system investment and operation planning and solving it with surrogate level-based Lagrangian relaxation to tame combinatorial complexity. It introduces a two-timescale model that embeds a rich unit-commitment representation within yearly investment decisions and employs SLBLR to decompose and coordinate subproblems efficiently. The results show that the proposed MILP+LR approach yields substantially different and lower-cost investment plans compared to RESOLVE, with earlier renewable and storage deployment and better emissions alignment. The work demonstrates the practical potential of advanced decomposition techniques to inform policy and planning for deep decarbonization, while highlighting the importance of accurate generator modeling for reliable, cost-effective pathways.

Abstract

With California's ambitious goal to achieve decarbonization of the electrical grid by the year 2045, significant challenges arise in power system investment planning. Existing modeling methods and software focus on computational efficiency, which is currently achieved by simplifying the associated unit commitment formulation. This may lead to unjustifiable inaccuracies in the cost and constraints of gas-fired generation operations, and may affect both the timing and the extent of investment in new resources, such as renewable energy and energy storage. To address this issue, this paper develops a more detailed and rigorous mixed-integer model, and more importantly, a solution methodology utilizing surrogate level-based Lagrangian relaxation to overcome the combinatorial complexity that results from the enhanced level of model detail. This allows us to optimize a model with approximately 12 million binary and 100 million total variables in under 48 hours. The investment plan is compared with those produced by E3's RESOLVE software, which is currently employed by the California Energy Commission and California Public Utilities Commission. Our model produces an investment plan that differs substantially from that of the existing method and saves California over 12 billion dollars over the investment horizon.

Figures (6)

• Figure 1: Visualization of the circular representation of time.
• Figure 2: Visualization of the ELCC surface.
• Figure 3: Comparison of CAISO fleet in 2045.
• Figure 4: Comparison of CAISO fleet over time.
• Figure 5: Battery state of charge for an exemplary week in 2045.