Raphael Rosen, The 10-Second Algorithm That Could Unlock Fusion Energy, Princeton Plasma Physics Laboratory, March 16, 2025.
QUADCOIL is a groundbreaking computer code that simplifies the design of stellarator magnets, ensuring plasma shapes remain practical for real-world construction.
Unlike traditional methods, it rapidly predicts magnet complexity, saving time and effort. By integrating engineering constraints early, QUADCOIL helps streamline the path to cost-effective fusion energy.
Rolnick D, Donti PL, Kaack LH, Kochanski K, Lacoste A, Sankaran K, Ross AS, Milojevic-Dupont N, Jaques N, Waldman-Brown A, et al. (2022) Tackling climate change with machine learning. ACM Computing Surveys (CSUR) 55(2):1–96.
Bill Gates, How to Avoid a Climate Disaster: The Solutions We Have and the Breakthroughs We Need, Random House, February 23, 2021.
J. Jin, Q. Wen, S. Cheng, Y. Qiu, X. Zhang, and X. Guo, Optimization of carbon emission reduction paths in the low-carbon power dispatching process, Renewable Energy, Volume 188, April 2022, Pages 425-436.
With the development of low-carbon electricity, the scale of wind power is expanding continuously and carbon trading for thermal power is popularized gradually. In this context, the optimal combination of thermal power and wind power needs to be further promoted to build up the synergy of carbon reduction. To solve such low-carbon power dispatching problem in the wind power integrated system imported with carbon trading, this paper firstly presents a distributed robust optimization model. Next, the scenario-based characterization of wind power and allocation methods of initial carbon emission rights are discussed for model solution. Finally, empirical analysis shows that: (1) the proposed model proves to be rational and feasible, which can accomplish a good compromise between economy, environment and robustness of power system, (2) wind power integration dose help carbon reduction ratio to achieve up to 50% with lower operating costs and carbon emissions, while carbon trading is really an effective approach for tapping greater carbon reduction potential of thermal power, and (3) more reasonable proportions of wind power in coping with its inherent uncertainties, and more appropriate cooperation modes of thermal power for dealing with carbon trading unpredictability are determined under the different requirement of carbon reduction.
F.A. Plazas-Niño, N.R. Ortiz-Pimiento, E.G. Montes-Páez, National energy system optimization modelling for decarbonization pathways analysis: A systematic literature review, Renewable and Sustainable Energy Reviews, Volume 162, July 2022, 112406.
Energy planning is fundamental to ensure a sustainable, affordable, and reliable energy mix for the future. Energy system optimization models (ESOMs) are the accurate tools to guide decision-making in national energy planning. This article presents a systematic literature review covering the main ESOMs, the input and output data involved, the trends in scenario analysis for decarbonization pathways in national economies, and the challenges associated with energy system optimization modelling. The first part introduces the characterization of ESOMs, showing a trend in modelling focused on long-term, multisector, multiperiod, bottom-up, linear programming, and perfect foresight. Secondly, the analysis shows the intensive data requirements, including future demand profiles, fuel price projections, energy potentials, and techno-economic characteristics of technologies. This review also reveals that decarbonization pathways are the principal objective in energy system optimization modelling, including key drivers such as high-share renewable energy integration, energy efficiency increase, sector coupling, and sustainable transport. The last section presents ten challenges and their corresponding opportunities in research, highlighting the improvement of spatiotemporal resolution, transparency, the inclusion of social aspects, the representation of developing country features, and quality and availability data.
Viola Becattini, Paolo Gabrielli, Cristina Antonini, Jordi Campos, Alberto Acquilino, Giovanni Sansavini, Marco Mazzotti, Carbon dioxide capture, transport and storage supply chains: Optimal economic and environmental performance of infrastructure rollout, International Journal of Greenhouse Gas Control, Volume 117, June 2022, 103635.
This work presents a novel optimization framework for the optimal design of carbon capture, transport, and storage supply chains in terms of installation, sizing and operation of carbon dioxide (CO2) capture and transport technologies. The optimal design problem is formulated as a mixed-integer linear program that minimizes the total costs of the supply chains while complying with different emissions reduction pathways over a deployment time horizon of 25 years. All design decisions are time-dependent and are taken with a yearly resolution. Whereas the model is general, here its features are illustrated by designing optimal supply chains to decarbonize the Swiss waste-to-energy sector, for various emission reduction pathways, when up to two storage sites are considered, namely one in the North Sea assumed to be already available and a hypothetical one in Switzerland assumed to be possibly available in the future. Findings show that, unless a domestic storage site becomes available soon, the transport cost is the greatest contribution to the overall costs, followed by the capture cost, while the storage cost plays only a minor role. Pipelines are the most cost-effective mode of transport for large volumes of transported CO2, especially when considering multi-year time horizons for the planning of the supply chains. Ship and barge connections are competitive with pipeline connections, whereas rail and truck connections are cost-optimal only when considering shortsighted time horizons or small volumes of CO2 transported.
Autopilot Safety
In 2021, we recorded 0.22 crashes for every million miles driven in which drivers were using Autopilot technology (Autosteer and active safety features). For drivers who were not using Autopilot technology (no Autosteer and active safety features), we recorded 0.77 crashes for every million miles driven. By comparison, NHTSA’s most recent data shows that in the United States there are 1.81 automobile crashes for every million miles driven.
David Rolnic et al., Tackling Climate Change with Machine Learning, ACM Computing Surveys, Volume 55, Issue 2, March 2023, Article No.: 42, pp 1–96, https://doi.org/10.1145/3485128
Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change.
採訪撰文 簡克志，美術設計 林洵安，機器學習 x 鈣鈦礦材料：讓 AI 幫你最佳化太陽能電池材料的製程參數！，研之有物，2022-02-21
機器學習輔助材料設計
為了 2050 淨零排放的目標，太陽能發電為不可或缺的再生能源之一，其中「鈣鈦礦太陽能電池」是近年最熱門的研究領域，不僅成本低廉、光電轉換效率也可達到 25%。然而，鈣鈦礦材料在環境中容易降解，影響使用壽命。材料科學家為了做出效能好又穩定的鈣鈦礦「料理」，無不卯足了勁，替這道菜加上各種「食材」，但是越複雜的菜，調出好味道就越困難。人腦畢竟有限，如果交給機器呢？中央研究院「研之有物」專訪院內應用科學研究中心包淳偉研究員，他與團隊訓練了一套機器學習模型，可以又快又準的找出複雜鈣鈦礦材料的最佳化條件！
David Cassel, Which Programming Languages Use the Least Electricity?, the New Stack, 20 May 2018.
Last year a team of six researchers in Portugal from three different universities decided to investigate this question, ultimately releasing a paper titled “Energy Efficiency Across Programming Languages.” They ran the solutions to 10 programming problems written in 27 different languages, while carefully monitoring how much electricity each one used — as well as its speed and memory usage.
Martina Fischetti, Jesper Runge Kristoffersen, Thomas Hjort, Michele Monaci, and David Pisinger, Vattenfall Optimizes Offshore Wind Farm Design, INFORMS Journal on Applied Analytics, 2020, Vol. 50, No. 1, pp. 80–94.
In this paper, we describe the use of operations research for offshore wind farm design in Vattenfall, one of the world’s leading companies in the generation of offshore wind energy. We focus on two key aspects that Vattenfall must address in its wind farm design process. The first is determining where to locate the turbines. This aspect is important because the placement of each turbine creates interference on the neighboring turbines, causing a power loss at the overall farm level. The optimizers must minimize this interference based on the wind conditions; however, they must also consider the other costs involved, which depend on factors such as the water depth or soil conditions at each position. The second aspect involves determining how to interconnect the turbines with cables (i.e., cable optimization). This requires Vattenfall to consider both the immediate costs and long-term costs connected with the electrical infrastructure. We developed mixed-integer programming models and matheuristic techniques to solve the two problems as they arise in practical applications. The resulting tools have given Vattenfall a competitive advantage at multiple levels. They facilitate increased revenues and reduced costs of approximately 10 million euros of net present value (NPV) per farm, while ensuring a much faster, more streamlined, and efficient design process. Considering only the sites that Vattenfall has already acquired using our optimization tools, the company experienced NPV gains of more than 150 million euros. This has contributed substantially to its competitiveness in offshore tenders and made green energy cheaper for its end customers. The tools have also been used to design the first wind farms that will be constructed subsidy-free.
Martina Fischetti and David Pisinger, Mathematical Optimization and Algorithms for Offshore Wind Farm Design: An Overview, Business & Information Systems Engineering, 2019, Vol.61, No. 4, pp. 469-485. (Further details)
M. Fischetti, Mixed-integer models and algorithms for wind farm layout optimization. Master’s thesis, University of Padova, 2014. (Stochastic programming for wake effect)
Fischetti M, Fischetti M (2016) Matheuristics. Mart´ı P, Panos P, Resende MG, eds. Handbook of Heuristics (Springer International Publishing, Cham, Switzerland), 1–33.
Broadly speaking, we see reinforcement learning delivering this value across the business, with potential applications in every business domain and industry (Exhibit 2). Some of the near-term applications for reinforcement learning fall into three categories: speeding design and product development, optimizing complex operations, and guiding customer interactions.
Exhibit 2 some applications.
To be sure, implementing reinforcement learning is a challenging technical pursuit. A successful reinforcement learning system today requires, in simple terms, three ingredients:
- A well-designed learning algorithm with a reward function.
- A learning environment.
- Compute power.
Computing power is better than compute power.
Make Solar Energy EconomicalPingWest，事關人類存亡的 14 大工程難題，要靠 AI 來搞定了，TechNews，2019 年 05 月 13 日
Provide Energy from Fusion
Develop Carbon Sequestration Methods
Manage the Nitrogen Cycle
Provide Access to Clean Water
Restore and Improve Urban Infrastructure
Advance Health Informatics
Engineer Better Medicines
Reverse-Engineer the Brain
Prevent Nuclear Terror
Secure Cyberspace
Enhance Virtual Reality
Advance Personalized Learning
Engineer the Tools of Scientific Discovery
Companies use AI to detect faults such as cracks in pipelines and machinery by analyzing images. This saves companies money and minimizes downtime when equipment breaks down, says Guido Jouret, chief digital officer of ABB, a Swiss power and automation company.
"An AI pilot project with one of the world's largest hydroelectric utilities showed a 10% reduction in routine maintenance and a 2% increase in output," he says. "These measures translate into millions of dollars in cost savings."...
Schneider Electric leverages Microsoft Corp.'s (MSFT) machine learning capabilities to monitor and configure pumps in the oil and gas field remotely since early detection of a pump failure can avoid weeks of the equipment being out of commission and repair costs of up to $1 million.
"Our customer Tata Power, India's largest power generator saved almost $300,000 on a single predictive maintenance catch," he says.
「台灣的電不乾淨嗎？」陸學森首先解釋一些關於電力的迷思。
事實上台灣只有 4% 的 PM2.5 來自電廠發電，然而有 25% 的 PM 2.5 來自汽機車排放；電動機車能源效率比較差嗎？錯了，燃油機車一公升最多只能跑 38 公里，但用在發電再儲存到電動機車，足足可以跑 85 公里。
「那電動機車會不會很耗電嗎？」但實際上一天的家庭總用電，其實就足夠給電動機車騎22天。電動機車平均一天騎起來只用0.56度，算起來就算全台1386萬台機車都變電動車了，也只佔台灣一天發電量 1.2%。但更特別的是，Gogoro 電網也跳開了全台一般的用電習慣，在晚上才充電去平衡電網需求。...
「初期有一大部分馬達就是士林電機幫我們做的。」陸學森也很自豪地說，Gogoro 高技術的馬達與零件也對台灣生產鏈起了升級作用。目前 Gogoro 自有生產線就有 48 組機器手臂、30 道工作模組，確實落實了工業 4.0 的概念。
談到換電，目前他們在台灣投資超過 105 億用於智慧電網裡，現在台灣 1200 站，預計年底前要鋪到 1500 站，完成全台車主平均 2 公里內就有站可以換電的目標。
