• Energy Research
• 2025-2025close
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• 7. Clean energy close
• 11. Sustainability close

We present a modeling and optimization framework to design powertrains for a family of electric vehicles, focusing on the concurrent sizing of their motors and batteries. Whilst tailoring these component modules to each individual vehicle type can minimize energy consumption, it can result in high production costs due to the variety of component modules to be realized for the family of vehicles, driving the Total Costs of Ownership (TCO) high. Against this backdrop, we explore modularity and standardization strategies whereby we jointly design unique motor and battery modules to be installed in all the vehicles in the family, using a different number of these modules when needed. Such an approach results in higher production volumes of the same component module, entailing significantly lower manufacturing costs due to Economy-of-Scale (EoS) effects, and hence a potentially lower TCO for the family of vehicles. To solve the resulting one-size-fits-all problem, we instantiate a nested framework consisting of an inner convex optimization routine which jointly optimizes the modules' sizes and the powertrain operation of the entire family, for given driving cycles and modules' multiplicities. Likewise, we devise an outer loop comparing each configuration to identify the minimum-TCO solution with global optimality guarantees. Finally, we showcase our framework on a case study for the Tesla vehicle family in a benchmark design problem, considering the Model S, Model 3, Model X, and Model Y. Our results show that, compared to an individually tailored design, the application of our concurrent design optimization framework achieves a significant reduction of the production costs for a minimal increase in operational costs, ultimately lowering the family TCO in the benchmark design problem by 3.5\%. 17 pages, 17 figures, 7 tables

Combustion occurring in porous media has various practical applications, such as in in-situ combustion processes in oil reservoirs, the combustion of biogas in sanitary landfills, and many others. A porous medium where combustion takes place can consist of layers with different physical properties. This study demonstrates that the initial value problem for a combustion model in a multi-layer porous medium has a unique solution, which is continuous with respect to the initial data and parameters in $\mathtt{L}^2(\mathbb{R})^n$. In summary, it establishes that the initial value problem is well-posed in $\mathtt{L}^2(\mathbb{R})^n$. The model is governed by a one-dimensional reaction-diffusion-convection system, where the unknowns are the temperatures in the layers. Previous studies have addressed the same problem in $\mathtt{H}^2(\mathbb{R})^n$. However, in this study, we solve the problem in a less restrictive space, namely $\mathtt{L}^2(\mathbb{R})^n$. The proof employs a novel approach to combustion problems in porous media, utilizing an evolution operator defined from the theory of semigroups in Hilbert space and Kato's theory for a well-posed associated initial value problem.

Machine learning's application in solar-thermal desalination is limited by data shortage and inconsistent analysis. This study develops an optimized dataset collection and analysis process for the representative solar still. By ultra-hydrophilic treatment on the condensation cover, the dataset collection process reduces the collection time by 83.3%. Over 1,000 datasets are collected, which is nearly one order of magnitude larger than up-to-date works. Then, a new interdisciplinary process flow is proposed. Some meaningful results are obtained that were not addressed by previous studies. It is found that Radom Forest might be a better choice for datasets larger than 1,000 due to both high accuracy and fast speed. Besides, the dataset range affects the quantified importance (weighted value) of factors significantly, with up to a 115% increment. Moreover, the results show that machine learning has a high accuracy on the extrapolation prediction of productivity, where the minimum mean relative prediction error is just around 4%. The results of this work not only show the necessity of the dataset characteristics' effect but also provide a standard process for studying solar-thermal desalination by machine learning, which would pave the way for interdisciplinary study.

Sustainable Development (SD) is a significant, high-visibility endeavor. However, there is no comprehensive synthesis of the concept. Instead, there is diversity and vagueness to the point where the term may have lost utility. The following dissertation investigates both theories and practices of SD, aiming to identify, validate, and apply a simple, informative, and operationalizable core idea behind SD. The research objectives are three-fold: (1) to propose a definition of SD that balances complexity and simplicity while accounting for variation in SD approaches; (2) to create an evaluation framework that effectively and minimally differentiates between adequate and inadequate approaches; and (3) to offer guidelines for SD implementation based on the framework’s application to selected case studies. The research consists of three studies, each focused on one of the objectives and presented in chapters two, three, and four.The first study seeks to identify a core concept for SD that is both simple and informative. The study argues that four concepts are fundamental to adequately understand SD: development, sustainability, justice, and governance. First, as a directional change in the net quality of life, development is needed to ensure desirable living standards for the overall population and subpopulations. Second, as the ability to maintain a system feature or state over a time horizon, sustainability is required to guarantee that the quality of life remains uncompromised in the face of social and environmental constraints and trade-offs. Third, justice is critical because while development can be unjust and injustices can be sustained, these outcomes are inconsistent with SD objectives. Fourth, governance is fundamental to regulate and enforce the desired traits of the system characterized by the previous three concepts, particularly to oppose existing tendencies toward inequity and injustice. The second study focuses on devising a diagnostic tool, i.e., an evaluation framework, to address the need to assess and compare the abundance of theoretical and practical approaches. Based on an intense interdisciplinary literature review, the developed framework consists of ten questions addressing development, sustainability, justice, and governance. The study argued that an adequate SD approach should cover and address all these questions promptly. The first two questions capture development by determining which metrics to use and how to measure developmental success. The next three questions cover sustainability by addressing what to sustain, how to determine success, and the intended time horizon. The following two questions focus on justice, addressing the target recipients and the distribution of benefits and burdens. Lastly, the final three questions encompass governance, including whether it is a shared practice, whether it results in collective actions toward SD, and whether SD objectives are integrated. Based on the analysis, SD is defined as Sustainable Development as the evolution of a particular Coupled Human and Natural System (CHANS) resulting from an intervention to improve or maintain the net quality of life for the entire system within the environmental and social constraints of the system, while ensuring that the increase in the relative quality of life for the least advantaged members of the system is at least greater than for the remainder, over multi-generation time horizons for system participants, and opposing existing tendencies toward inequity and injustice via appropriate governance. The third study applies the evaluation framework to two case studies, the Burning Man Project (BMP) and the UN’s Sustainable Development Goals (SDGs), to demonstrate the diagnostic tools’ utility, validate its robustness, and extract cross-scales insights. The investigation reveals that both BMP and SDGs meet the minimum criteria for adequate SD approaches despite implicit or partial responses to the ten questions. Recommendations for improving these approaches include explicating the time horizon of sustainability and a more thorough integration of objectives. Furthermore, based on the comparison of the two cases, the study discusses the role of the population, the importance of integrating objectives, and scale considerations and provides practical insights. For instance, both BMP and SDGs need to uphold a minimum commitment among their target population to their respective practices and ensure meeting sustainability constraints. However, the reasons, potential solutions, and management mechanisms differ between the two cases due to context-dependent factors and variations in scale. The aspiration is that this research offers a foundation for future investigation and application of SD, providing a comprehensive and operationalizable framework to evaluate and compare diverse SD approaches and facilitating more effective development and implementation of SD strategies. The insights gained from this study can help inform the development of new SD approaches, monitoring and assessment tools, and foster a more nuanced understanding of the intricacies inherent in the pursuit of SD.

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.

Optimally scheduling multi-energy flow is an effective method to utilize renewable energy sources (RES) and improve the stability and economy of integrated energy systems (IES). However, the stable demand-supply of IES faces challenges from uncertainties that arise from RES and loads, as well as the increasing impact of cyber-attacks with advanced information and communication technologies adoption. To address these challenges, this paper proposes an innovative model-free resilience scheduling method based on state-adversarial deep reinforcement learning (DRL) for integrated demand response (IDR)-enabled IES. The proposed method designs an IDR program to explore the interaction ability of electricity-gas-heat flexible loads. Additionally, the state-adversarial Markov decision process (SA-MDP) model characterizes the energy scheduling problem of IES under cyber-attack, incorporating cyber-attacks as adversaries directly into the scheduling process. The state-adversarial soft actor-critic (SA-SAC) algorithm is proposed to mitigate the impact of cyber-attacks on the scheduling strategy, integrating adversarial training into the learning process to against cyber-attacks. Simulation results demonstrate that our method is capable of adequately addressing the uncertainties resulting from RES and loads, mitigating the impact of cyber-attacks on the scheduling strategy, and ensuring a stable demand supply for various energy sources. Moreover, the proposed method demonstrates resilience against cyber-attacks. Compared to the original soft actor-critic (SAC) algorithm, it achieves a 10% improvement in economic performance under cyber-attack scenarios. Accepted by Applied Energy, Manuscript ID: APEN-D-24-03080

While the operating cost of electricity grids based on thermal generation was largely driven by the cost of fuel, as renewable penetration increases, ancillary services represent an increasingly large proportion of the running costs. Electric frequency is an important magnitude in highly renewable grids, as it becomes more volatile and therefore the cost related to maintaining it within safe bounds has significantly increased. So far, costs for frequency-containment ancillary services have been socialised in most countries, but it has become relevant to rethink this regulatory arrangement. In this paper, we discuss the issue of cost allocation for these services, highlighting the need to evolve towards a causation-based regulatory framework. We argue that parties responsible for creating the need for ancillary services should bear these costs. However, this would imply an important change in electricity market policy, therefore it is necessary to understand the impact on current and future investments on generation, as well as on electricity tariffs. Here we provide a mostly qualitative analysis of this issue, defining guidelines for practical implementation and further study. Published in journal Energy Policy

A nuclear reactor based on MIT BEAVRS benchmark was used as a typical power generating Pressurized Water Reactor (PWR). The PARCS v3.2 nodal-diffusion core simulator was used as a full-core reactor physics solver to emulate the operation of a reactor and to generate training, and validation data for the ANN. The ANN was implemented with dedicated Python 3.8 code with Google's TensorFlow 2.0 library. The effort was based to a large extent on the process of appropriate automatic transformation of data generated by PARCS simulator, which was later used in the process of the ANN development. Various methods that allow obtaining better accuracy of the ANN predicted results were studied, such as trying different ANN architectures to find the optimal number of neurons in the hidden layers of the network. Results were later compared with the architectures proposed in the literature. For the selected best architecture predictions were made for different core parameters and their dependence on core loading patterns. In this study, a special focus was put on the prediction of the fuel cycle length for a given core loading pattern, as it can be considered one of the targets for plant economic operation. For instance, the length of a single fuel cycle depending on the initial core loading pattern was predicted with very good accuracy (>99%). This work contributes to the exploration of the usefulness of neural networks in solving nuclear reactor design problems. Thanks to the application of ANN, designers can avoid using an excessive amount of core simulator runs and more rapidly explore the space of possible solutions before performing more detailed design considerations.