Que: Kindly explain how AI can improve the integration of renewable energy sources into traditional power grids and how AspenTech solutions differentiate from its industry competitors?

Ans: Driven by the energy transition towards decarbonization, the Indian government’s goal to reach 500GW of renewable energy capacity by 2030 is accelerating the critical modernization of the power grid. Digital solutions, such as advanced analytics and AI, are rapidly reshaping energy systems – offering new ways to optimize the planning and operation of distributed energy resources (DERs). As renewable energy adoption grows, challenges such as grid stability, energy distribution, and bidirectional flow integration are becoming more complex. AI technologies, including machine learning (e.g., neural networks), are proving essential for advanced energy management, outage, contingency, and congestion detection, as well as predictive maintenance.

In energy management, these technologies, which enable more accurate demand forecasting, supply and load balancing, and renewable integration – are mitigating operational challenges, associated with incorporating renewable energy into traditional power grids. With grid modernization, AI can better predict renewable output based on weather patterns and historical performance, allowing for optimal supply planning at any given time. Real-time demand insights are becoming increasingly critical as intermittent renewable resources make up a growing portion of the power mix.
AI can also enhance decision support in energy management, reducing risks, costs, and efforts associated with congestion management. Additional AI use cases include optimizing outage prediction and crew dispatching to improve system reliability for end customers as well as grid maintenance.

AspenTech’s vision for the utility of the future starts with enhancing our Digital Grid Management suite to expand beyond the control room, to create a self-optimizing grid. Here, we will shift focus beyond the control room to the near-adjacency utility functions where real-time operational data becomes the crucial catalyst for unleashing new capabilities and value in planning and maintenance functions. This cutting-edge point of view will help position companies ahead of the industry curve.

Que: Elaborate on how AI can optimize resource allocation during periods of high demand in renewable energy operations and what role does AspenTech’s CCUS solution play in enhancing performance in sustainability?

Ans: AspenTech incorporates a style of AI – known as Industrial AI – that combines industrial and scientific domain expertise with AI to provide guardrails that ensure accuracy, reliability and safety. Industrial AI provides enhanced visibility and control of the grid. These digital systems enable utilities to produce, transmit and distribute power, while maintaining grid stability and reliability – in the face of increasing demand and the variable nature of renewable energy sources. When it comes to resource allocation, Industrial AI can improve accelerated real time state estimation situational awareness. This can lead to decision support tools to optimize problem solving around congestion management in the operational processes. Topological remedial strategies can be suggested by exploring various grid topology findings and validating the solution by means of load flow. Down the road, further options to switching are possible, such as further VAR dispatching, curtailing solar, ramping up storage or providing new pricing to DERs etc.

More specifically, Industrial AI helps to further penetration of renewables and is a tool for optimization. Its use is gaining traction globally to enable CCUS at a wider scale. Applying Industrial AI in carbon capture processes can provide agility in screening thousands of innovation options – allowing companies to quickly evaluate thousands of possible solvents and project designs. In identifying the most efficient, economical, and scalable processes, such tools are helping to develop more efficient carbon capture solutions.

For example, AspenTech is using AI and machine learning approaches to speed up material discovery for CO2 capture and low-carbon pathways. In operations, simulation tools combined with edge instrumentation and hybrid modeling, which combines AI with first principles, enables digital twins to provide feedback towards improving economics, performance and reliability.
AspenTech’s CCUS solution is also utilized by EPCs to design multiple carbon capture facilities. For example, Technology Centre Mongstad, the largest carbon capture demonstration facility in the world uses AspenTech Performance Engineering solutions to support the optimization of carbon capture technologies. Moreover, commercially available technologies today were developed using AspenTech process simulation tools, including Shell’s Cansolv and Fluor’s Econamine solvents. Finally, Direct Air Capture (DAC) companies like Carbon Engineering and Carbon Capture, Inc. have relied on AspenTech to improve the economics of the technologies and support commercial-scale development.

Que: How are AI guardrails designed to prevent models from proposing solutions that violate physical laws in energy operations?

Ans: Industrial AI stems from combining AI with engineering fundamentals and results in crucial domain-expertise driven guardrails. Results follow the scientific laws, ensuring that outcomes are safe. Industrial AI works around the premises of agility, guidance and automation – for example, as an expert co-pilot and advisor to help workers make better, more strategic decisions, faster.

In contrast to common practice that includes constraints in the cost function during training, we integrate guardrails, for example enabling mass balance in hybrid models, within the architecture of the algorithms themselves, ensuring that they cannot be violated during operation. With built-in guardrails to ensure plant operations run safely, Industrial AI models can also help companies drive efficiencies, track equipment health and optimize sustainability.

Que: What AI-based tools are used to track carbon emissions and energy efficiency in renewable energy plants?

Ans: Renewable energy plants must first conduct a comprehensive assessment of their operations to identify areas with the highest potential for improvement. For example, by analyzing energy-intensive processes and pinpointing inefficiencies. Industrial AI can impact predictive maintenance, where it anticipates equipment failures and optimizes operational schedules to reduce downtime and energy use. In supply chain optimization, Industrial AI can help lower emissions and costs across asset-intensive processes, all of which leverage capabilities in data analysis and process optimization to achieve significant sustainability gains.

By using predictive analytics, renewable energy plants can forecast energy demand and optimize usage. Industrial AI can also monitor emissions in real-time, identifying excessive sources and suggesting corrective actions. Machine learning models can analyze production processes to improve efficiency and reduce wastage. AI-driven simulations can explore operational scenarios, enabling the plant to adopt strategies that minimize their environmental impact. By continuously learning and adapting, Industrial AI-based systems help renewable energy plants stay on top of their sustainability goals and dynamically adjust their operations for better environmental performance. Regular audits and updates to AI models ensure that the latest data and trends are incorporated.

Que: In what ways does AI facilitate the scaling of renewable energy projects in countries with diverse demographics?

Ans: Industrial AI can enable the scaling of renewable energy projects by ensuring that businesses can respond rapidly to market demands and operational changes. For example, in countries with diverse demographics, Industrial AI uplevels the workforce to guide them daily and make complex decisions faster. Industrial AI-led software can assist new employees in understanding system optimizations and support experienced staff in managing complex processes. Such systems not only accelerate decision-making, but also improve accuracy in daily and complex scenarios via a systematic approach.

Industrial AI-powered automation frees engineers from routine tasks, allowing them to focus on higher-value activities. One practical application is the automatic segmentation and interpretation of sub-surface image volumes. This enables businesses to efficiently identify optimal locations for CO2 storage, driving sustainability efforts. The ability of Industrial AI to enhance agility, provide insightful guidance and automate routing tasks significantly boosts operational efficiency and strategic decision-making, positioning businesses to achieve greater success and sustainability.

Que: Do you have any new AI technologies that are on the horizon for optimizing renewable energy systems in the near future?

Ans: At this point, we do not have specifics to share. It is notable that AI plays a crucial role in embedding sustainability into a corporate strategy by providing data-driven insights that inform decision-making. AI-powered tools can evaluate the environmental impact of various business activities, allowing renewable energy systems to make informed choices that align with their sustainability goals.