Moving towards active energy management

Moving from passive to active energy management can transform energy usage in industrial environments and can help reduce the environmental impact of a process and meet sustainability goals.

By Alain Hermans April 9, 2024
In the Control Engineering webcast SCADA series: New SCADA features and functions,” Matt Ruth, president at Avanceon, and Nicholas Imfeld, operations manager at Avanceon, discussed current trends and innovations pertaining to SCADA technology. Courtesy: CFE Media and Technology

Energy management insights

  • There is a shift from passive to active energy management in industries, aiming to reduce environmental impact and meet sustainability goals by optimizing energy usage.
  • The utilization of real-time data, smart technologies and energy management software for monitoring, analyzing and optimizing energy consumption in production facilities will be important.

The drive toward reducing energy costs and achieving net-zero sustainability goals is a significant trend across most industries today. Rethinking how energy usage is monitored and managed is crucial for achieving net-zero objectives.

Historically, manufacturers approached energy management more passively, meaning that energy consumption was not a primary focus, and practices tended to prioritize production efficiency and cost reduction over sustainability. Several factors, including a revenue-centric focus, contributed to this passive approach, along with limited awareness about climate change and abundant and inexpensive energy.

However, today this historical status quo has changed with many organizations now actively re-evaluating their energy usage – primarily driven by rapidly rising energy costs and tightening environmental targets. This has resulted in a transition from passive to active energy management, with strategies being implemented to enhance efficiency, reduce environmental impact and meet sustainability goals.

Having a good understanding about energy usage in production facilities demands more than just utility invoice data and manual meter readings. This passive approach will only provide limited data and will give an incomplete picture of how and when energy is being used. More than that, the data you do get likely is not tied to what is happening on the production floor which makes it challenging to link energy consumption to production outcomes.

However, with better use of their existing infrastructure, standard up-gradation of legacy systems and integration of new smart technologies, it is possible to manage energy usage within an organization.

Once data is made available it then needs to be put into the correct context, which can be achieved with tools such as energy management software to inform process improvements. For example, patterns can be uncovered once baseline energy usage is established and the top energy consuming phases in a production line can be identified. This can help address profit and loss verticals of efficiency, savings and throughput maximization, but all of these key performance indicators (KPIs) can also contribute to achieving broader sustainability goals.

An active energy management approach uses real-time data to monitor analyze and optimize energy consumption, allowing organizations to make informed decisions, implement energy-saving measures and respond dynamically to fluctuations in energy demand.

Central to an active energy management approach is the ability to deploy real-time monitoring, which can be achieved by utilizing sensors, meters and smart devices to continuously monitor energy usage across various processes and equipment in real-time. Armed with this information, data analytics tools can be deployed to process and analyze energy data, creating visualizations and dashboards to provide timely, clear and actionable representations of energy consumption patterns.

Areas of energy inefficiency – such as equipment running at suboptimal levels, power spikes during non-peak hours or anomalies in consumption patterns – can then be identified and rectified. Strategies can also be implemented to dynamically adjust energy usage in response to real-time conditions, such as demand response programs that encourage load shedding during peak times.

The next steps

Real-time energy data is critical to optimizing energy usage in industrial operations. But there are other opportunities to layer in new technologies to realize more significant savings.

Technologies like machine learning (ML) and artificial intelligence (AI) can guide control system responses and reveal continuous improvement opportunities. This can help to dynamically adjust processes in real-time based on predicted conditions, making it possible to optimize sustainability, productivity and quality all at once.

For example, the Eastern Municipal Water District (EMWD) in California, in the US, saved an estimated 2,330 kilowatt hours of electricity per day and $100,000 per year using an AI-enabled control system. The control system – deployed at one of EMWD’s four reclamation facilities – continuously monitors and learns the current state of operations and makes automatic adjustments for best performance as conditions change.

Sustainability at scale

Improving environmental performance within an operation involves identifying and addressing areas of energy loss and inefficiency. Central to this strategy are energy audits. By conducting regular audits to identify areas of energy waste and inefficiency, it is possible to assess where to target remedial action. Adopting a comprehensive approach to energy monitoring and conservation can help reduce operational costs, minimize environmental impact and contribute to sustainable practices.

For many organizations, the difficulty is where to start an active energy management journey. A sensible starting point is to first understand the existing infrastructure. Assess the machinery, sensors and meters to identify what data they produce and how that data can support energy management needs. An assessment may reveal, for example, gaps in the network infrastructure that need to be closed to allow energy data to be sent seamlessly from the edge to the cloud.

When a gap that needs to be filled is identified – whether in hardware, software or in the network, consider how potential solutions to bridge that gap will benefit the overall approach to energy management.

Integrating real-time energy data and advanced control capabilities into daily operations transforms sustainability optimization from a one-time effort to an ongoing and integral part of the business process. This shift represents a strategic approach where sustainability is deeply embedded in the organization’s culture, operations and decision-making.

In essence, combining real-time energy data and advanced control capabilities will transform sustainability optimization into a dynamic and integral aspect of the business. It goes beyond compliance and cost reduction, fostering a holistic approach to environmental responsibility that aligns with long-term business objectives.

The total energy footprint of a manufacturing facility is indeed influenced by various phases, and understanding each phase is crucial for a comprehensive approach to sustainability. A holistic and integrated approach that spans the entire lifecycle of a product allows manufacturers to identify opportunities for energy reduction and implement sustainable practices at each stage. This approach not only helps in reducing operational costs but also aligns with environmental stewardship and corporate social responsibility goals.

Original content can be found at Control Engineering Europe.

Author Bio: Alain Hermans is Process Industry Strategy & Marketing Manager for EMEA at Rockwell Automation.