Three of America’s largest cities recently hired for the hottest job on the market: Chief Heat Officer. With each new summer season, it’s becoming more and more obvious how much hotter summer days have become, and how rapidly our global climate is changing. If it was not clear before, now more than ever, we need to manage our way through a global warming crisis.

This raises an important double-ended conundrum for folks responsible for providing us energy. As the world gets warmer, we use more energy to cool down, which in turn makes the world even warmer. On the one hand, generating power and electricity, particularly using fossil fuel sources, is responsible for a significant portion of global warming. According to the UN, fossil fuels account for 75% of global greenhouse gas emissions and 90% of carbon dioxide emissions. Both of these emissions are largely responsible for our quickly warming planet. On the other hand, with hotter summers, we rely on air conditioning and need to generate more energy to cool down, straining our electricity generating capacity. And so the warming trap continues. We need an intelligent solution to this problem. Enter Artificial Intelligence (AI).

Given that it takes time to evolve the infrastructure we use to generate energy and that we desperately need to generate more energy, to address the warming trap in the short term, we need to consider methods to better use our limited energy resources. In other words, we make better use of the energy we currently generate as we aim to reduce our reliance on fossil fuels and also free up more energy to cool us during our hotter summers. AI provides some immediate solutions in the management and optimization of energy transmission systems. Here are some interesting suggesitons in which AI-enhanced systems can have a significant impact:

AI systems are well equipped to analyze vast amounts of data from sensors, smart meters, and other sources to monitor energy used by homes, industries, businesses and other sectors in the US. In fact, by 2027, we expect 93% of all homes in the US to have smart meters that monitor the amount of electricity the home uses in real time.

With this amount of information gathered through high smart meter use in the US, we can develop AI models that monitor in real-time grid conditions, and that can predict and dynamically optimize power flows. In fact, given the already high smart meter penetration in homes across the US, power companies may already have enough data develop fine-tuned predictions of energy use over the next few weeks, based on past energy uses in response to weather and climate conditions. With this in mind, it is possible to design AI systems that better transmit energy to better match customers needs and that may even augment residential energy usage to best utilize our limited energy resources. 

In fact, in 2022, California pioneered a system where homeowners are sent texts to reduce power usage to reduce strain on its power system. Homeowners and other power users in California were sent warning texts to turn off and conserve energy to avoid a black out. Customers responded by reducing their energy usage resulting in an immediate and significant drop in power usage. Enhancing these existing capabilities with AI-enhanced models will create more dynamism in our energy transmission systems and even empower users to actually drive energy conservation.

Before we can reduce to zero fossil fuels burned to produce electricity, experts estimates $21 trillion worth of investments are required in order to overhaul the aging global electrical grid infrastructure. Needless to say, $21 trillion is a pretty penny, representing something of a bottleneck to progress. How then can we manage our current grid infrastructure in the meantime? Grid control systems enhanced by machine learning models can use massive amounts of grid control and sensor data to detect potential equipment failures or anomalies in aging energy transmission infrastructure. Grid operators will use this information to prevent further damage before it occurs, implement proactive solutions to better utilize transmission resources and reduce downtime by preempting costly failures and outages from occurring.

Utilities companies that have implemented early machine learning predictive assets into their operations have halved transformer failures, achieved up to $800,000 in annual costs savings and gained over $40 million in estimated economic value from optimized operational and capital expenditures.

In addition, [AI systems can enhance the resilience of energy transmission networks by dynamically adapting to changes and disruptions. Through real-time monitoring and analysis, AI algorithms can identify grid disturbances, reroute power flows, and isolate affected areas to minimize the impact of disruptions and restore power quickly. This self-healing capability improves grid reliability, reduces outage durations, and enhances overall system resilience.]

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Preventing Climate Disaster & Protecting Our Planet: AI Can Prevent Catastrophic Forest Fires and Disaster Events

AI-powered dynamic line rating systems enable real-time monitoring of environmental conditions and power flow to optimize the capacity of transmission lines. By accurately assessing weather conditions, wind speed, temperature, and other factors, AI algorithms can adjust line ratings, allowing higher power flows when conditions permit. This increases the utilization of existing infrastructure, improves grid efficiency, and reduces the need for costly infrastructure upgrades.

Energy Flow Forecasting

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