| Author: | Yang, Yijie |
| Title: | Data-driven low-carbon operation of energy systems under uncertainty |
| Advisors: | Wang, Dan (COMP) |
| Degree: | Ph.D. |
| Year: | 2026 |
| Department: | Department of Computing |
| Pages: | xviii, 175 pages : color illustrations |
| Language: | English |
| Abstract: | Energy systems produce a significant share of greenhouse gas (GHG) emissions. To mitigate the impacts of climate change, it is imperative to curtail the carbon dioxide emissions originating from the energy sector. Despite the insights offered by existing research efforts, two important gaps remained. The first gap lies in the lack of incorporation of a carbon perspective in the operation of energy systems. Previous optimization problems are mainly cost-aware. New carbon-oriented operation strategies and algorithms need to be developed that allow decision-makers to define and plan the necessary short-term actions toward the goal of achieving zero-carbon, as well as to track progress over time. And this needs to coordinate different components and precisely calculate the carbon emissions within the energy systems. Another challenge lies in the uncertainties inherent in predictions. Downstream operational optimization problems, such as those relevant to energy systems, often require upstream predictions—for example, day-ahead forecasts for renewable energy generation, electricity load, electricity price, and carbon intensity of the main grid—as inputs. In this paper, we investigate carbon-aware operation scheduling for multi-energy building integrated energy systems (BIES), thermostatically controlled loads (TCLs), and data center energy systems (DCES). Firstly, we investigate carbon-aware scheduling for multi-energy BIES. We explore achieving net-zero emissions through the carbon-aware optimal scheduling of the multi-energy BIES. We integrate advanced technologies and strategies, such as the carbon capture system (CCS), power-to-gas (P2G), carbon tracking, and emission allowance trading, into the traditional BIES scheduling problem. The proposed model enables accurate accounting of carbon emissions associated with building energy systems and facilitates the implementation of low-carbon operations. Furthermore, to address the challenge of accurately assessing uncertainty sets related to forecasting errors of loads, generation, and carbon intensity, we develop a learning-based robust optimization approach for BIES that is robust in the presence of uncertainty and guarantees statistical feasibility. The proposed approach comprises a shape learning stage and a shape calibration stage to generate an optimal uncertainty set that ensures favorable results from a statistical perspective. Numerical studies conducted based on both synthetic and real-world datasets have demonstrated that the approach yields up to 8.2% cost reduction, compared with conventional methods, in assisting buildings to robustly reach net-zero emissions. Secondly, we focus on the carbon reduction potential of TCLs, which include air conditioners, heat pumps, water heaters, and refrigerators, play a pivotal role in demand response due to their thermal inertia and inherent flexibility. TCLs also substantially impact energy consumption and emissions within commercial and residential buildings, which makes them critical for the low-/zero-carbon transition that the building sector is undergoing to meet global climate objectives. To aid in this process, this paper proposes a carbon-aware robust scheduling approach for TCLs. The proposed approach precisely models carbon emissions attributed to TCLs, and formulates TCL scheduling as a distributionally robust chance-constrained (DRCC) optimization problem to ensure robust decision-making. We then develop a novel bilevel optimization reformulation strategy to address challenges such as over-conservatism and computational intractability that often arise from solving DRCC problems using conventional approaches. Real-world data evaluation demonstrates significant reductions in costs and carbon emissions compared to state-of-the-art methods, showcasing the effectiveness of our approach in potentially decarbonizing the building sector. Third, we explore the carbon-aware scenario in data centers. The rapid growth of AI applications is dramatically increasing data center energy demand, exacerbating carbon emissions, and necessitating a shift towards 24/7 carbon-free energy (CFE). Unlike traditional annual energy matching, 24/7 CFE requires matching real-time electricity consumption with clean energy generation every hour, presenting significant challenges due to the inherent variability and forecasting errors of renewable energy sources. Traditional robust and data-driven optimization methods often fail to leverage the features of the prediction model (also known as contextual or covariate information) when constructing the uncertainty set, leading to overly conservative operational decisions. We propose a comprehensive approach for 24/7 CFE data center operation scheduling, focusing on robust decision-making under renewable generation uncertainty. This framework leverages covariate information through a multi-variable conformal prediction (CP) technique to construct statistically valid and adaptive uncertainty sets for renewable forecasts. The uncertainty sets directly inform the chance-constrained programming (CCP) problem, ensuring that chance constraints are met with a specified probability. We further establish theoretical underpinnings connecting the CP-generated uncertainty sets to the statistical feasibility guarantees of the CCP. Numerical results highlight the benefits of this covariate-aware approach, demonstrating up to 6.65% cost reduction and 6.96% decrease in carbon-based energy usage compared to conventional covariate-independent methods, thereby enabling data centers to progress toward 24/7 CFE. Finally, we focused on nuclear-powered data center energy systems. The rapid rise of AI applications has driven data centers to unprecedented energy demands, which has prompted major tech companies to adopt on-site nuclear power plants (NPPs) alongside grid electricity. While existing research focuses on off-site NPPs in multi-energy systems optimized for investment returns, recent advances in small modular reactors (SMRs), particularly load-following SMRs (LF-SMRs), offer flexible, reliable power tailored for datacenter co-location. However, LF-SMRs are governed by a set of physical constraints, such as ramp rate and stability limits, making them unsuitable as fully dispatchable sources. We propose a novel day-ahead workload scheduling approach that jointly coordinates datacenter operations and LF-SMR output, explicitly modeling these constraints. We develop a two-stage formulation that forecasts carbon-free grid energy from the grid using conformal prediction in the first stage and then optimizes LF-SMR output and workload scheduling via mixed-integer programming in the second stage. Evaluation on real workload traces shows that our method reduces carbon-based energy consumption by up to 43.44% compared to baselines that omit nuclear integration or ignore SMR limitations. In summary, we propose four methods to overcome the challenges brought by the uncertainty of predictions in carbon-aware scheduling of different energy systems. All these methods are implemented and evaluated with real-world datasets, and the experimental results present the effectiveness of the proposed methods. |
| Rights: | All rights reserved |
| Access: | open access |
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