Optimizing Deep Decarbonization Pathways in California with Power System Planning Using Surrogate Level-based Lagrangian Relaxation
Osten Anderson, Nanpeng Yu, Mikhail Bragin

TL;DR
This paper presents a detailed optimization model for California's power system decarbonization, using a novel surrogate Lagrangian relaxation method to handle large-scale complexity and improve investment planning accuracy.
Contribution
It introduces a rigorous mixed-integer model and a surrogate level-based Lagrangian relaxation solution approach for large-scale power system investment optimization.
Findings
Achieves optimization of a 12 million variable model in under 48 hours.
Produces an investment plan significantly different from existing methods.
Saves California over 12 billion dollars in power system investments.
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…
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Taxonomy
TopicsElectric Power System Optimization · Integrated Energy Systems Optimization · Optimal Power Flow Distribution
MethodsFocus
