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.

The rapid expansion of generative artificial intelligence (AI) technologies is projected to have a significant effect on electricity use in the global data center sector. Some 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 AI data center’s potential roles in a clean energy system. Our work finds that the levelized cost of computing (LCOC) favors higher load factors and shows 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 market price conditions and variations. The findings reveal that a boom in AI energy use could drive significant baseload power demand in the future power system.

A document by Jonas Noeland . Click on the document to view its contents.

This paper presents a novel method for using Graph Neural Networks in combination with reinforcement learning for power reliability studies. Monte Carlo methods are the backbone of such probabilistic power system reliability analyses. Recent efforts from the authors have shown that it is possible to replace Optimal Power Flow solvers with the policies of deep reinforcement learning agents and obtain significant speedups of Monte Carlo simulations while retaining close to optimal accuracies. However, a limitation of this reinforcement learning approach is that the training of the agent is tightly connected to the specific case being analyzed, and the agent cannot be used as is in new, unseen cases. In this paper, we seek to overcome these issues. First, we represent the state and actions in the power reliability environment by features in a graph, where the adjacency matrix can vary from time step to time step. Second, we train the agent by applying a message passing graph neural network architecture to an integrated variant of an actor-critic algorithm. This implies that the agent can solve the problem independently of the power system grid structure. Third, we show that the agent can solve small extensions of a test case without having seen the new parts of the power system during training. In all of our reliability Monte Carlo simulations using this  graph neural network agent, the simulation time is competitive with that based on optimal power flow, while still retaining close to optimal accuracy.

Industrial demand response will become increasingly important in power grids with high shares of variable renewables, yet the existing knowledge on how the industrial electricity demand and flexibility will change with the decarbonization of chemical processes is limited. Here we develop a mixed-integer linear optimization model, which we use to compare the cost and flexibility of the most relevant decarbonization options for the combined chlor-alkali electrolysis (CAE) and vinyl chloride monomer (VCM) production process. We combine product and energy storage to enable the full flexibility potential of the decarbonized process. Our results show that flexible operation of the CAE process is deemed technically possible but limited by internal process dependencies due to decarbonization of the VCM production. Combining energy and product storage for demand response enables up to 4% operational cost reduction by shifting loads during peak price hours. High overcapacity of PEM electrolyzers are required to release the full flexibility potential in the hydrogen based decarbonization option, while the less flexible direct electrification option shows a potential for OPEX reduction. Full decarbonization of the combined CAE and VCM process without increasing operational cost significantly appears difficult. Our study emphasizes demand response through product and energy storages as a viable pathway for minimizing the added cost, and also enables a significant reduction of electric demand in high-price hours.

The operation of energy storage systems (ESSs) in power systems where variable renewable energy sources (VRESs) and ESSs must contribute to securing the supply, can be considered as an arbitrage against scarcity. The value of using stored energy instantly must be balanced against its potential future value and future risk of scarcity. This paper proposes a multi-stage stochastic programming model for the operation of microgrids with VRESs, ESSs and thermal generators that is divided into a short- and a long-term model. The short-term model utilizes information from forecasts updated every six hours, while the long-term model considers the value of stored energy beyond the forecast horizon. The model is solved using stochastic dual dynamic programming and Markov chains, and the results show that the significance of accounting for short- and long-term uncertainty increases for systems with a high degree of variable renewable generation and ESSs and limited dispatchable generation capacity.

We present a medium-term hydropower scheduling model that includes state-dependent environmental constraints on maximum discharge. A stochastic dynamic programming algorithm is used to enable modelling of nonconvex relationships in the problem formulation. The model is applied in a case study of a Norwegian hydropower system with multiple reservoirs. We find that the maximum discharge constraint significantly impacts the water values and simulated operation of the hydropower system. A main finding is that the nonconvex characteristics of the environmental constraint is reflected in the water values, implying a nonconvex objective function. Operation according to the computed water values is simulated for cases with and without the environmental constraint. Even though operation of the system changes considerably when the environmental constraint is included, the total electricity generation over the year is kept constant, and the total loss in expected profit limited to less than 0.8%.