Energy Conglomerate Maximizes Operational Capacity-based Maintenance Scheduling Model

Problem

A major power plant in Africa operates 18 gas engines with a total installed capacity of 175 megawatts (MW), using natural gas as its primary fuel. This plant is one of the largest of its kind in the region. In recent years, the South African electricity utility, Eskom, introduced a program that allows independent power producers to sell their excess capacity back to the national grid on a short-term basis. This initiative presented an opportunity for the plant to contribute more power and help reduce strain on the national electricity grid, enhancing its reputation as a low-carbon energy producer.

However, managing the maintenance of such a large facility poses significant challenges. The gas engines require maintenance at specific intervals based on their asset management plans. The service intervals vary depending on the type of maintenance needed, and the duration of each service differs. Starting all 18 engines at the same time resulted in overlapping maintenance schedules, leading to high demand for maintenance teams. The plant, however, only had one core maintenance team available, making it difficult to meet all the service requirements effectively.

To improve operational efficiency and increase power generation capacity, a solution was needed to optimize the maintenance schedules, ensuring that the engines remained operational while adhering to necessary service intervals.

Solution

The power plant’s internal team was tasked with developing an optimized maintenance scheduling system for the gas engines. Given the complexity of the different service intervals and the varying durations of each shutdown, the scheduling problem had to be approached with a long-term perspective, often spanning two years or more.

This task posed a mixed-integer scheduling problem, requiring advanced computational tools to solve. Additionally, the solution had to be user-friendly so that non-technical staff could easily run the scheduling tool as needed.

The chosen tool for this challenge was a robust optimization software that integrates multiple solvers. This tool provided the flexibility and computational power required to handle the large-scale scheduling problem, while also offering a user-friendly interface to simplify the process for operators.

Working with an international consulting group specializing in optimization solutions, the team successfully developed a scheduling application that allowed for the efficient planning of maintenance operations. The tool helped to create an optimized schedule for the gas engines, minimizing downtime and maximizing electricity production.

Results

The implementation of this optimized scheduling solution led to several key improvements:

• Increased electricity production during winter: By optimizing maintenance schedules, the plant was able to increase its electricity production during high-demand winter months by 4.6%.
• Enhanced operational capacity: The optimization reduced downtime, allowing the plant to contribute more power to the national grid during critical periods, helping to alleviate pressure on the grid.
• Improved ability to meet demand: The optimized schedule ensured that engines were available when demand was high, enhancing the plant’s ability to meet electricity needs.
• Adherence to service intervals: The tool ensured that all engines received the necessary maintenance at the correct intervals, helping to prevent potential breakdowns or inefficiencies.
• Optimal use of the maintenance team: The solution allowed for better coordination of the limited maintenance team, ensuring that resources were used efficiently during service periods.

This approach highlights how advanced scheduling and optimization tools can significantly improve operational efficiency, especially in industries with complex maintenance needs. By minimizing downtime and streamlining maintenance processes, power plants can increase their productivity and better support energy demands.