From: 2024-10-16 10:30 to 11:30
Place: Seminar Room M 3170-73 at Dept. of Automatic Control, LTH
Contact: johan [dot] lindberg [at] control [dot] lth [dot] se

Date & Time: October 16th, 10:30 - 11:30
Location: Seminar Room M 3170-73 at Dept. of Automatic Control, LTH. 
Author: Noah Åkesson
Title: Linear Programing and Robust Optimization of Electricity Resources on the Swedish Electricity Market
Supervisor: Johan Lindberg, Max Nilsson, Erik Östberg (Modity)
Examiner: Pontus Giselsson

Zoom link: https://lu-se.zoom.us/j/67821729950

Abstract: 
The efficient allocation of flexible resources, such as grid batteries and wind parks, across multiple energy market auctions, is pivotal for maximizing socio-economic welfare, interpreted here as profits. This study explores various optimization methods, including Linear Programming, Integer Linear Programming, Robust Optimization with Linear Programming, and Robust Optimization with Integer Linear Programming, to determine their efficacy in different market conditions. Initially, under the assumption of perfect price information, Linear Programming and Integer Linear Programming methods were applied to optimize allocations for both wind parks and battery storage systems. The resulting optimization models, tested on two days with different characteristics, demonstrated that optimized allocations across multiple markets significantly enhanced profitability, with the wind park achieving profits of approximately 80,000 EUR compared to 1,400 EUR when solely allocated to the spot market on 2024-04-10.

In conclusion, optimized allocation of flexible resources across multiple markets enhances profitability. LP emerges as a practical and efficient method for real-world applications, supporting traders in decision-making processes. However, integer linear programming and robust optimization methods present limitations under uncertainty and computational constraints, emphasizing the need for improved forecasting models and adaptive optimization strategies. Future research should focus on enhancing price forecasting accuracy and exploring alternative robust optimization approaches to further optimize profitability and efficiency in dynamic energy markets.