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Applied energy 388, 125530 (2025). doi:10.1016/j.apenergy.2025.125530 Published by Elsevier Science, Amsterdam [u.a.]

Applied energy 389, 125764 (2025). doi:10.1016/j.apenergy.2025.125764 Published by Elsevier Science, Amsterdam [u.a.]

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.

This study presents a techno-economic assessment of dynamic wireless power transfer for long-haul freight transport, focusing on the fleet operator’s perspective. In particular, we compared three different powertrain technologies: a conventional powertrain and a battery-electric with or without a dynamic charger installed. For all three technologies, we developed a cost model to assess the total cost of ownership for a fleet operator using different scenarios. Notably, dedicated cost models were devised to estimate energy carrier costs and costs related to time loss incurred by fleet operators due to extended delivery times of electric trucks compared to conventional ones. The novelties in the cost model are twofold. First, dedicated cost models have been devised to estimate the costs related to the energy carriers (including the cost of infrastructure) and to the time loss incurred by fleet operators due to the extended delivery times of electric trucks compared to conventional ones. Second, the energy consumption by source and travel time were derived from an ad-hoc developed simulation approach models longitudinal dynamics of the case-study as well as the powertrain’s performance on the basis of experimentally derived look-up tables provided by manufacturers as well as by previous research projects. The simulation results provided by this model are instrumental to our enhanced cost model as it provides the required inputs and it allowed us to tailor the results to a specific delivery mission. Our results provide valuable insights for fleet operators considering the adoption of zero-emission trucks and to policy-makers and other infrastructure stakeholders regarding the conditions required for the cost-effectiveness of electric road systems.