  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.
