Hydropower producers rely on stochastic optimization when scheduling their resources over long periods of time. Due to its computational complexity, the optimization problem is normally cast as a stochastic linear program. In a future power market with more volatile power prices, it becomes increasingly important to capture parts of the hydropower operational characteristics that are not easily linearized, e.g., unit commitment and nonconvex generation curves. Stochastic dual dynamic programming (SDDP) is a state-of-the-art algorithm for long- and medium-term hydropower scheduling with a linear problem formulation. A recently proposed extension of the SDDP method known as stochastic dual dynamic integer programming (SDDiP) has proven convergence also in the nonconvex case. We apply the SDDiP algorithm to the medium-term hydropower scheduling (MTHS) problem and elaborate on how to incorporate stagewise-dependent stochastic variables on the right-hand sides and the objective of the optimization problem. Finally, we demonstrate the capability of the SDDiP algorithm on a case study for a Norwegian hydropower producer. The case study demonstrates that it is possible but time-consuming to solve the MTHS problem to optimality. However, the case study shows that a new type of cut, known as strengthened Benders cut, significantly contributes to close the optimality gap compared to classical Benders cuts.

The rapid expansion of generative artificial intelligence (AI) technologies is projected to significantly affect electricity use in the global data center sector. Earlier research has proposed using data centers for load-balancing the future power grid to allow higher integration of variable renewables. In this paper, we review the expected future electricity consumption of AI and evaluate the behavior of AI data centers in clean energy systems. Our work found that the levelized cost of computing (LCOC) favors higher load factors and shows a relatively low sensitivity to electricity price levels. Under our baseline cost assumptions, a baseload electricity price of $125/MWh benefits load factors higher than 64%, depending on the market price conditions and variations. Nevertheless, high-tier data centers with higher operational costs and capital expenditures favor even higher load factors to optimize their LCOC. These findings show that a boom in AI energy use could drive significant baseload power demand in future power systems.

There is an unprecedented need to expand the toolbox of solutions to boost the scalability of clean power and energy systems. Amidst this challenge, nuclear energy is increasingly recognized as an important player in the path toward deep decarbonization of the global energy mix. This paper presents a technology review of small modular reactor (SMR) concepts currently under development and deployment. Both conventional and next-generation reactor technologies are evaluated with a focus on potential power system integration benefits, added system values, and provision of system-bearing services. Nevertheless, there are currently some uncertainties in the techno-economic competitiveness of SMRs, whether they can leverage economics of mass production over their inherent lack of economics of scale. To address this challenge, our paper provides some basic cost analysis of SMRs, taking into account their expected learning curves to evaluate the cost of future deployments and give insights into their economic competitiveness and potential role as a disruptive solution in the energy transition.

Small modular reactors (SMRs) are currently touted as the next evolution of nuclear energy. They are a type of nuclear reactor designed to be smaller in size than a traditional reactor. Nevertheless, they are based on the same fission technology that splits atoms to create heat, which can then be used to generate electricity. SMRs can offer clean, reliable power and heat at manageable costs for both on- and off-grid communities. Currently, there are more than 100 proposed SMR reactor designs worldwide participating in the race to commercialization. This article presents a comprehensive review of the different SMR technologies that are currently being developed. The technical characteristics of the SMRs have been evaluated to provide insights into their complementary role in a renewable energy system and highlight how they can accelerate the journey toward a deeply decarbonized world.

The future power grid will integrate large shares of variable renewables, such as solar and wind, through inverter-based solutions. These developments change the power grid’s characteristics owing to lower shares of classical synchronous generators and result in weaker, low-inertia power grids that challenge their secure and robust operation. To deal with this unsolved challenge, there is a potential for small modular reactors (SMRs) to provide system-bearing ancillary services, such as uncontrolled physical inertia and short circuit capacity, that can enable large-scale penetration of renewables. This article shows that SMRs can avoid the need for synchronous condensers to run renewable-rich power grids, thereby reducing ancillary service costs by approximately $20/MWh and simultaneously ensuring higher grid strength and inertia. Consequently, running renewable-rich power grids with SMRs as a backbone can be a competitive grid-forming solution in the journey toward a deeply decarbonized power system.