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

Background: Machine Learning (ML) techniques have successfully replaced traditional control algorithms in recent years due to their ability to carry out complicated tasks with significant efficiency and accuracy. Objective: The main objective of the current work is to investigate and compare the performances of different ML models in modeling Maximum Power Point Tracking (MPPT) control for a wind turbine system. The main advantage of the designed MPPT based on ML is that it does not require any detailed mathematical model or prior knowledge of the system, such as turbine parameters or aerodynamic properties, unlike traditional MPPT techniques. Methods: The ML models included in this study were Support Vector Machines, Regression Trees, and Ensemble Trees. Their design was performed through a training process, and their performances were evaluated based on various metrics. During the training phase, the ML models were selected to understand the basic concept of the control strategy and extract essential hidden connections between the inputs and the output of the system. Results: The effectiveness of the control method was investigated using MATLAB/Simulink. The findings of this study revealed that ML models were effective in modeling the MPPT for the studied wind power system, which provides an interesting and sophisticated alternative to classical control methods for wind systems. Conclusion: The ML models designed allow for optimal operation of the system with a simple structure that is independent of system parameters and wind speed measurement and is adaptable for any kind of system.

Background: In recent years, the integration of renewable energy sources into the grid has increased exponentially. However, one significant challenge in integrating these renewable sources into the grid is intermittency. Objective: To address this challenge, accurate PV power forecasting techniques are crucial for operations and maintenance and day-to-day operations monitoring in solar plants. Methods: In the present work, a hybrid approach that combines Deep Learning (DL) and Numerical Weather Prediction (NWP) with electrical models for PV power forecasting is proposed Results: The outcomes of the study involve evaluating the performance of the proposed model in comparison to a Physical model and a DL model for predicting solar PV power one day ahead and two days ahead. The results indicate that the prediction accuracy of PV power decreases and the error rates increase when forecasting two days ahead, as compared to one day ahead. Conclusion: The obtained results demonstrate that DL models combined with NWP and electrical models can improve PV Power forecasting compared to a Physical model and a DL model.

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

The increasing wildfire activity in the past few years has been devastating, setting negative records in many states and regions around the world, especially in North America. Power systems have been impacted by wildfires in many ways, even in regions located hundreds of kilometers away from high-risk zones, depending on wind speed and direction conditions, the stemming smoke of wildfires may significantly impact the air quality and reduce the solar PV generation, and forcing several utilities to rely on PSPS programs to mitigate wildfire risks. Thus, power system operators must ensure reliability and resilience across power generation, transmission, and distribution while minimizing carbon emissions that can harm even more the air quality of the affected communities during wildfire events. Furthermore, a cost-effective power system expansion planning solution in regions with increased wildfire risk is achieved by placing ESSs and new transmission/distribution lines while taking into account their availability given the increasing number of PSPS events. This research aims to analyze the impact of wildfire activity on the electrical system's planning and operation, by analyzing the impact of the 2020 wildfire season on renewable energy in Washington state, focusing on variables that directly impact the wind and photovoltaic power. After that, efforts are made to approach the expansion planning of power transmission and distribution systems under wildfire risk, considering sitting and sizing of ESS as an alternative, with a compliance check on unbalanced power flow and system operating limits. The resulting models are a MILP optimization problem, and simulation experiments are performed to validate the effectiveness of the proposed formulation using different High Fire-Threat District Tier Zones based on real-world data from electric utilities in California.

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