(Original article from Medium)

Introduction

With rising energy costs, market volatility and increasing emphasis on sustainable practices, manufacturers are under pressure to optimize energy use. Traditional methods like peak shaving and load shifting have long been used to manage energy consumption. While effective in specific scenarios, these methods can disrupt production and even increase wear on equipment.

However, with the rise of artificial intelligence (AI) and advanced data analytics, a new approach has emerged: AI-powered dynamic energy optimization. Companies like BeChained are pioneering this field, offering solutions that adjust energy usage in production processes in real-time without compromising production schedules or operational stability.

This article explores the fundamental differences between traditional peak shaving and AI-powered energy optimization, highlighting how advanced AI-driven techniques enable efficient energy use without disrupting production.

Peak Shaving: Reducing Energy Use with Production Interruptions

How Peak Shaving Works

Peak shaving is a straightforward method to mitigate high electricity demand charges by managing power loads during peak times. This is often done through:

• Supply Disconnection: Disconnecting or “shedding” specific machinery or production lines to instantly reduce energy draw.
• Load Prioritization: Choosing less critical machines or processes to disconnect when electricity demand reaches a certain threshold.

This technique is common in industries with high-energy demands, as it effectively lowers energy costs, but harnesses operation continuity.

Drawbacks of Peak Shaving

While peak shaving helps reduce electricity costs, it comes with notable disadvantages:

1. Production Interruptions: Machines disconnected during peak periods cause workflow disruptions, impacting production schedules and potentially reducing output quality.
2. Equipment Wear: Frequent starts and stops on machinery can lead to excessive wear and reduce the overall lifespan of equipment.
3. Process Instability: Reconnecting machines after peak periods can lead to unplanned fluctuations in production, affecting consistency and stability.
4. Set-up costs: Restarting machines after a shutdown generates more costs to fine tuning continuous standard production. Scrap, energy peak and initial lower quality output need to be accounted by manufacturers.

While effective in specific use cases, peak shaving isn’t an ideal long-term or frequent solution, especially for industries that prioritize operational stability and consistent production output.

Energy Usage Pattern for Peak Shaving

Below is a simplified graphical representation of energy consumption over time under a peak-shaving strategy. Notice the distinct drops in energy usage during peak hours, indicating moments when machinery is disconnected from production.

Load Shifting: Moving Energy-Intensive Tasks to Off-Peak Hours

How Load Shifting Works

Load shifting focuses on redistributing energy demand to less costly times rather than reducing the total energy used. For example:

• Rescheduling Production: Moving certain high-energy tasks, like heating or cooling, to off-peak hours.
• Batching Processes: Consolidating energy-intensive tasks into specific times when energy costs are lower.

Load shifting is a strategic solution for industries that have flexibility in their production schedules. By moving demand away from peak periods, load shifting can reduce costs without requiring machines to be disconnected. However, it’s only effective when production schedules allow flexibility, and not all industries can easily adapt their workflows in this way.

Drawbacks of Load Shifting

• Limited Application: Only works if production schedules can be adjusted.
• Risk of Overlapping Peaks: Shifting processes may inadvertently create a new peak if not managed carefully.
• Process Delays: Rescheduling production can introduce inefficiencies if workflows are time-sensitive.

AI-Powered Dynamic Energy Optimization: Intelligent, Continuous Efficiency Without Production Impact

How AI-Powered Dynamic Optimization Works

Unlike peak shaving or load shifting, AI-powered optimization doesn’t require rescheduling or disconnection. Instead, AI dynamically adjusts machine settings to reduce energy consumption without disrupting production. Here’s how it works:

1. Continuous Fine-Tuning: AI makes real-time adjustments to machine settings, balancing efficiency and productivity without altering production schedules.
2. Enhanced Process Efficiency: The AI analyzes historical and real-time data on energy use, production output, and equipment status to identify energy-saving opportunities without compromising quality or throughput.
3. Adaptive Learning: By continuously learning from data, the AI adapts to changes in demand, equipment performance, on-the-run actions and environmental factors, dynamically optimizing energy use.

This AI-driven approach is designed to operate seamlessly within any production schedule, eliminating the need for either disconnection (as in peak shaving) or rescheduling (as in load shifting). It fine-tunes machine’s energy use throughout a production line in real time, resulting in reduced costs and emissions.

Benefits of AI-Powered Dynamic Optimization

• No Production Interruption: AI-driven optimization doesn’t require disconnecting or rescheduling equipment, so production can continue uninterrupted.
• Lower Equipment Wear: With fewer starts and stops, equipment experiences less strain and a longer lifespan.
• Increased Efficiency and Stability: The AI makes precise, incremental adjustments that avoid sharp spikes or drops in energy usage, contributing to a more stable production environment.
• Consistent Energy Savings: AI continuously identifies new opportunities for optimization, even as production demands shift.

Comparison of Energy Management Techniques

Visualizing the Difference in Energy Usage Over Time

Let’s compare the energy usage patterns of peak shaving, load shifting, and AI-powered dynamic optimization:

• Peak Shaving Graph: Shows energy usage dropping sharply at specific times, indicating temporary machine disconnections. These disconnections result in clear production interruptions, as energy usage suddenly decreases and then rebounds.
• Load Shifting Graph: Displays a redistribution of energy usage over time, with certain tasks being shifted to off-peak hours. Energy use is shifted rather than reduced, potentially leading to new peaks at adjusted times if not managed carefully.
• AI Optimization Graph: Shows a steady, continuous reduction in energy consumption without sharp drops or peaks. This represents fine-tuning of machine settings to optimize energy use without impacting production schedules.

Choosing the Right Energy Management Strategy

Conclusion: The Future of Industrial Energy Optimization

For manufacturers seeking a long-term, sustainable, non-disruptive approach to energy management, AI-powered dynamic optimization represents the most powerful alternative to traditional peak shaving and load shifting. Unlike methods that disrupt production or require flexible scheduling, AI-driven solutions like BeChained’s optimize energy usage dynamically. This approach is a trade off for companies to reduce energy use without compromising productivity.

By shifting from traditional approaches to adaptive, continuous optimization, manufacturers step into more efficient and sustainable future. AI-powered dynamic optimization truly stands out as the most adaptable and production-friendly solution for the modern industrial landscape.