Muse? Newsletter #21 - AI-Driven & Smart grids

A smart grid is a network that integrates energy distribution and digital technologies in a two-way flow of electricity and data.

In a conventional energy distribution network, production is controlled solely at the source of energy production. In a smart grid, however, energy production is controlled by the various players involved.

Leveraging artificial intelligence in smart grid management creates opportunities for optimized energy processes, predictive maintenance, and efficient demand-side management.

AI-driven predictive analytics can anticipate and mitigate potential grid failures, optimize energy generation and distribution, and enable proactive maintenance strategies, leading to cost savings, reduced carbon emissions, and improved resource utilization. Additionally, AI-powered optimization algorithms can optimize energy dispatch, grid congestion management, and demand response programs, further enhancing grid efficiency and resilience.

The integration of AI into smart grid management is significantly enhancing the latter’s engineering capabilities and market performance. On the engineering front, AI improves predictive maintenance and fault detection by using machine learning models that analyze data from sensors and IoT devices to predict equipment failures before they occur. This enhances reliability and reduces downtime. Additionally, AI optimizes energy distribution by forecasting load accurately with neural networks and time series analysis, and by dynamically adjusting energy flows to balance supply and demand in real-time. In renewable energy, AI-powered smart inverters and controllers increase the efficiency of energy conversion and storage, while AI systems manage grid stability by handling the variability and intermittency of renewable sources.?

On the market side, AI supports demand response programs by analyzing consumer energy usage patterns to design incentives for reduced consumption during peak times, and by enabling dynamic pricing models that balance load and reduce costs. Energy trading platforms become more efficient and secure through the integration of AI with blockchain technology, creating transparent peer-to-peer trading environments. AI predicts energy market trends, aiding utilities and consumers in making informed trading decisions. AI-driven analytics provide personalized energy-saving recommendations, while AI-powered chatbots handle customer inquiries and service requests, improving customer engagement and satisfaction.

However, significant challenges exist in integrating AI into existing grid infrastructures due to their complexity, heterogeneity, and cybersecurity concerns. Another challenge lies in ensuring transparency, accountability, and ethical use of AI algorithms in decision-making processes. As AI algorithms become more autonomous and decentralized, concerns arise regarding interpretability, bias, and unintended consequences. Transparency mechanisms, algorithmic explainability tools, and ethical guidelines are crucial to foster trust and confidence in AI-driven decision-making. Overcoming these challenges requires investments in explainable AI, robust data management frameworks, and scalable computing infrastructure to enable real-time decision-making and optimization in smart grid operations.?

Furthermore, the skills gap and fostering interdisciplinary collaboration between energy experts, data scientists, policymakers, and AI ethicists is essential to developing AI solutions that prioritize societal values and sustainability.

Collaborative initiatives between government agencies, industry stakeholders, and research institutions are needed to establish standards, guidelines, and best practices for AI deployment in smart grid management.

Regulatory and policy frameworks play a critical role in incentivizing investments in AI-driven smart grid technologies, ensuring interoperability, and safeguarding consumer privacy rights.

In the BAI WInter School at Caltech CTME on contextual AI, we will discuss the applications, opportunities and challenges of leveraging AI in smart grid management.

At Caltech CTME, an interdisciplinary team of engineers, economists, mathematicians, and computer scientists, supported by the Resnick Sustainability Institute, Southern California Edison, and the Department of Energy, have been pioneering over the last decade the development of devices, systems, theories, and algorithms to create a distributed and interconnected intelligent grid capable of sensing, generating, and responding to the ever-changing dynamics of the energy market.

We remind you that AI Muse? Grenoble discussion group and our website is available at the following address: Muse?: Listen to your muse.

If you would like to continue the discussion, feel free to contact Lee Schlenker and Alexandre MARTIN.

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Toward a Smarter Grid | California Institute of Technology (CalTech)

This article, published in October 2015, features an introduction to smart grids by Professor Steven Low, a presentation of the working group established at CalTech, and various case studies.

Professor Steven Low, a faculty member at CalTech, discusses the future of electricity generation and the inception of the project within CalTech. He also presents specific cases encountered by teams from the Resnick Sustainability Institute and their external partners.

The first part of the article provides an overview of smart grids by Steven Low, who explains their functionality and significance. He elaborates on the increasing complexity of Cyber-Physical Systems (CPS) and the resulting advantages and disadvantages.

In Part 2, the article highlights the work being conducted at CalTech under the Resnick Sustainability Institute's leadership, focusing on the creation of an interdisciplinary group. This group consists of engineers, economists, mathematicians, and computer scientists, and is further supported by partners from both the private and public sectors. The final section of the article illustrates the Institute’s case studies.?

Learning-Augmented Control and Decision-Making: Theory and Applications in Smart Grids | Tongxin Li / California Institute of Technology (CalTech)

America’s Department of Energy reckons that ai and other improvements to the country’s existing grid could liberate as much as 100 gigawatts (gw) in transmission and distribution capacity over the next three to five years without the need to build new lines.

That is about 13% of current peak demand of around 740gw.

How AI is transforming the smart grid | Daveed Sidhu

In this LinkedIn post, Daveed Sidhu argues that the smart grid benefits from AI's ability to optimize generation, transmission, and distribution of electricity while providing consumers with insights into their energy usage and production.?

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Use of artificial intelligence in smart grids | Samudrapom Dam

Samudrapom Dam suggests that AI enables optimized energy processes, predictive maintenance, and efficient demand-side management in smart grids, enhancing energy system resilience and sustainability.

However, challenges such as the complexity of integrating AI into existing grid infrastructures, data privacy concerns, and limitations in AI techniques need to be addressed.

A comprehensive review of AI-enhanced smart grid integration for hydrogen energy | ScienceDirect

In this article published in the International Journal of Hydrogen Energy, the authors review the integration of AI technology with smart grids to optimize hydrogen energy utilization, addressing advancements, challenges, and potential breakthroughs.

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Role of artificial intelligence in grid modernization | ResearchGate / Suman Mysore

The global economy's progression and the increasing reliance on grid-dependent technologies have intensified electricity demand, making manual grid management both challenging and costly.

However, artificial intelligence (AI) emerges as a promising solution to automate various grid processes effectively.

Partner

The BAI Winter School at Caltech

The?Business Analytics Institute, together with the ZHAW School of Management and Law and ?Caltech's CTME, will be offering a 10-day winter school next February 3-12 on the Caltech campus in Pasadena, CA on leveraging contextual AI in business and society.

This university and corporate accredited Winter School will help both management students?and confirmed managers improve their practice of data science in business and society.

Each course will be facilitated by industry-recognized experts in their fields.? Company visits and professional speakers from the industry leaders will highlight this session.??

The Session is hosted by Caltech’s Center for Technology & Management Education. Nestled in the ‘Crown of the Valley’ in Southern California, Pasadena is known for its rich cultural, social, and entertainment centres , including the Norton Simon Museum, the Huntington Library, and Old Town Pasadena.

Our white paper on Contextual AI, obtain your free copy of our Executive Report on “Contextual AI”. Click here to obtain your free copy.

Quick facts:

• The Winter Session will be held February 3-12, 2025. We will focus on the design, production, and evaluation of contextually based AI solutions in the using case studies, workshops, seminars, and expert testimony from CalTech.
• The session is open to PhD and graduate management and engineering students, as well as working professionals aiming to develop their practical application of Data Science and Artificial Intelligence in business and society.
• This winter’s international faculty will include Profs. Cielen, Hefner, Hitz, and Schlenker.
• Company speakers and/or visits are planned with the data science teams from leading international companies in AI.
• Our cultural visits will include the Huntington Library, the Jet Propulsion Laboratory, Universal Studios, a gourmet cooking class, and a professional sporting event.
• The practicums will be run using Colab, GitHub, Python and LangChain.
• The basic fees for the session are 1895 euros for the class fees and social activities. Several negotiated housing options are available to enrolled studentss.
• Student Registration closes October 28, 2024. Early bird discounts are available to students registering before June 28th.

More information => https://www.baieurope.com/winter-school-25

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