AI’s Impact On Energy Systems – CleanTechnica Exclusive

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Artificial intelligence (AI) is transforming almost every industry, and the energy sector is no exception. AI’s impact may revolutionize the way we generate, distribute, and consume energy. It can also guide the energy industry to become more efficient, cost effective, and sustainable.

Energy systems across the world are undergoing a shift toward clean and sustainable energy sources. Technical and organizational level changes along with technological upgrades in various sectors such as energy generation, transmission, and distribution are becoming commonplace. That means there has also been a rise in engineering challenges to create a sustainable energy system that takes into account social, economic, and environmental factors.

AI’s impact on these factors in energy systems is becoming quite influential. Expanding the applications of AI technology across the power and energy sector is promoting better control and management of energy consumption, anticipating network malfunctions, or even optimization. Machine learning (ML) can make fine-grained determinations of what customers want and then adjust energy purchasing decisions accordingly.

Recognizing the potential of AI’s impact, we were pleased to have the chance to chat with Rahul Kar, Chief Operating Officer at AutoGrid.

Start us off. In the broadest of senses, how is artificial intelligence (AI) starting to play a major role in the energy market?

The application of AI/ML in the energy sector is not entirely new and started around 2010 with the deployment of Internet-connected devices like smart meters, thermostats, and controllers. Additionally, initial applications of AI/ML focused on forecasting, loads, customer behavior, and generation. Organizations like AutoGrid took advantage of the deluge of data that was generated over the years and added advances to both optimization and operations research to manage the grid. Widespread investment in AI across the energy sector continues to directly impact and advance grid resiliency, along with the overall adoption of renewables.

In just over a decade, the application of AI to the energy sector is producing dramatic results and achieving outcomes that would be impossible for humans to replicate. The scale of the electric grid alone can overwhelm traditional resources, with millions of discrete endpoints all interacting in real time to maintain narrow frequency tolerances. As the grid grows increasingly complex, the need for AI only deepens.

What do you foresee as AI’s impact on electric energy systems, electric vehicles (EVs), sustainable development goals (SDGs), and greenhouse gas (GHG) emissions over the next decade?

Increasing consumer adoption of distributed energy resources (DERs), such as electric vehicles (EVs) and residential solar and storage, is significantly transforming the structure of energy delivery. Traditionally, energy was generated, stored, and delivered to consumers in a standard fashion, and usage was measured with a meter. However, with the combination of DERs and AI, operators are now able to see what’s happening behind the meter and also forecast usage to manage grid stability.

With AI-powered software driving grid optimization, we will leave no electrons behind through an increasingly powerful virtualization layer. Advanced predictive controls enable utilities, energy providers, and grid operators to optimize DERs and manage them as a single system. Aggregated into a virtual power plant, diverse sources of distributed energy such as electric vehicles, solar PV, batteries, and demand response programs can balance supply and demand, reduce peak load, improve grid reliability, and create new value streams for prosumers and energy providers alike. It is only through the proliferation of AI-powered VPPs around the globe that we will one day reach 100% renewables.

In the US, the power industry has started using AI to connect with smart meters, smart grids, and the Internet of Things devices. How are these AI technologies improving efficiency, energy management, transparency, and the usage of renewable energies?

Renewable energy resources have typically been balanced with fossil fuels to ensure the stability and reliability of grid systems. However, with AI-powered virtual power plants (VPPs), operators can forecast and optimize energy use and connect and manage DERs for additional capacity to ensure resilience in times of unstable energy supply. Together, AI and VPPs are ending the paradox of both managing the intermittency of renewables and the race to electrify everything with environmentally harmful solutions like peaker plants.

Please talk us through the idea that leveraging a diverse portfolio of distributed energy resources (DERs) — including demand response, renewable energy, energy storage systems, and traditional energy sources — can create virtual power plants (VPPs) that expand or contract based on the needs of wholesale or retail energy markets. And where does AI’s impact come in?

With a more diverse mix of DERs entering the market, VPPs are becoming more robust, which enables the technology to provide as much energy to grids as traditional power plants. This wouldn’t be possible without AI technology, which removes the complexity of converging not only distributed but also diverse energy resources to provide seamless management from a centralized dashboard. AI ensures that DERs can be harnessed at scale and in real-time.

Thanks to AI and ML, a state-of-the-art VPP is like running tens of thousands of power plants in parallel, ensuring they all operate cooperatively. All the software functions required for a centralized peaker plant must be replicated thousands of times, and the complexity increases not linearly but exponentially. While this is a daunting task, the deployment of commercial projects based on AI and ML technology is occurring worldwide. ML and AI algorithms are instrumental in efficiently managing this complexity, by orchestrating the aggregated resources and ensuring seamless coordination across the VPP. Real-time decision-making and control become possible, allowing the VPP to respond rapidly to grid conditions and optimize energy flows.

How can AI contribute to increasing energy efficiency and lowering energy consumption in wind energy production, for example? Multiple factors contribute to the randomness, volatility, and intermittent nature of wind power generation and make wind energy prediction difficult. How does AI help to mitigate the complexity and uncertainty of the causes of wind in nature?

Wind forecasting is currently a challenging but also very localized problem. However, as more advanced modeling and AI systems emerge, it will become even easier to predict wind generation.

AI algorithms also consider the temporal and spatial aspects of DER fleets. These algorithms account for fluctuations in renewable generation such as solar and wind, variations in energy consumption patterns from devices such as air conditioners and heat pumps, and the dynamic nature of grid conditions, including extreme peaks in demand. By continuously adapting and recalibrating based on real-time data, AI ensures that the dispatch and coordination of DER assets remain responsive, flexible, and efficient.

AI is starting to be integrated in monitoring and data processing systems for fault diagnosis and detection to help mitigate impact to solar PV systems, especially when undesirable weather conditions are anticipated. What can you tell us about this potential?

AI algorithms can also analyze weather patterns and predict energy production from these variable resources, allowing operators to adjust their grid systems to accommodate expected fluctuations in supply as well as enable real-time adjustments.

In addition to solar PV systems, any smart technology with monitoring capabilities that is connected to the grid can diagnose imminent failure due to weather conditions. Combined with AI technology, these capabilities will reduce technician deployments across sites and subsequently cut the operational expenses of running grids significantly. Utilities will experience the majority of these savings, which will likely be appreciated by consumers.

Source: CleanTechnica

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