Maximizing the Profit for Industrial Customers of Providing Operation Services in Electric Power Systems via a Parallel Particle Swarm Optimization Algorithm

Abstract

Integration of renewable energy sources require an increase in the flexibility of power systems. Demand response is a valuable flexible resource that is not currently being fully exploited. Small and medium industrial consumers can deliver a wide range of underused flexibility resources associated with the electricity consumption in their production processes. Flexible resources should compete in liberalized operation markets to ensure the reliability of the system at a minimum cost. This paper presents a new tool to assist industrial demand response to participate in operation markets and optimize its value. The tool uses a combined physical-mathematical modelling of the industrial demand response and a Parallel Particle Swarm Optimization algorithm specifically tuned for the proposed problem to maximize the profit. The main advantages of the proposed tool are demonstrated in the paper through its application to the participation of a meat factory in the Spanish tertiary reserve market during a whole year using a quarter-hourly time resolution. The enhanced performance of the proposed tool with respect to previous methodologies is shown with these four flexible processes examples, where the maximum available profit obtained in the simultaneous consideration of all different flexible processes is computed. The flexible processes are technical and economically characterized in a way that makes the tool valid for most of the processes in the industry.

Keywords

• Demand response
• energy resource management
• industrial production
• end-user tool
• parallel particle swarm optimization