Summary

In his presentation, Sebastian Johann discusses the optimization of energy generation for district heating networks. He explains the journey from traditional management methods to advanced optimization solutions, emphasizing the importance of meeting heating demand while maximizing economic benefits. The presentation outlines the company's transition to modern, automated systems and the significant improvements achieved through mathematical optimization.

To gain deeper insights and access exclusive content, we encourage you to fill out the form and unlock more valuable information.

Challenges

The transition from traditional to optimized energy generation faced several challenges. Traditionally, plant managers relied on fixed-price contracts and on-the-spot decisions to manage heating networks. This approach was feasible due to the low volatility in electricity and gas prices. However, participating in the day-ahead market required accurate forecasts and timely decision-making. The need to satisfy heating demand at all times added complexity, especially when integrating renewable energy sources and fluctuating market prices.

Solution

To address these challenges, the company developed a comprehensive optimization model. This model includes a digital twin of the plant, heat demand forecasts using both traditional and AI-based methods, and price forecasts for electricity and gas. The optimization model, implemented in the Azure cloud, uses a mixed-integer optimization algorithm to generate schedules that maximize economic efficiency while ensuring heating demands are met. The transition involved building in-house expertise, developing cloud-based solutions, and creating a central data management system to streamline operations.

Results

The implementation of the optimized energy generation model resulted in full automation of schedule creation and execution, improved economic performance, and enhanced transparency and stability. The optimized schedules allowed for better utilization of heat storage and flexible operation of CHP units, leading to significant cost savings and increased revenues. The comparison of optimized schedules with traditional methods showed notable improvements, especially in summer when flexibility is higher. The company also achieved better long-term planning and profitability calculations, supporting strategic decisions.