Alberizzi, A.; Di Barba, P.; Mognaschi, M.E.; Zani, A. Optimization of an Agent-Based Model for Continuous Trading Energy Market. Electrical Engineering 2024, doi:10.1007/s00202-023-02202-w.
Alberizzi, A.; Di Barba, P.; Mognaschi, M.E.; Zani, A. Optimization of an Agent-Based Model for Continuous Trading Energy Market. Electrical Engineering 2024, doi:10.1007/s00202-023-02202-w.
Alberizzi, A.; Di Barba, P.; Mognaschi, M.E.; Zani, A. Optimization of an Agent-Based Model for Continuous Trading Energy Market. Electrical Engineering 2024, doi:10.1007/s00202-023-02202-w.
Alberizzi, A.; Di Barba, P.; Mognaschi, M.E.; Zani, A. Optimization of an Agent-Based Model for Continuous Trading Energy Market. Electrical Engineering 2024, doi:10.1007/s00202-023-02202-w.
Abstract
The high penetration of intermittence resources in the energy market accelerates significantly the decarbonization process but, on the other hand, the electrical system has to face the problem of unbalances. Renewable Energies Sources (RES) are hard to precisely forecast, and power plants are not able to predict the amount of energy that they can provide far from the real time delivery. In this frame, the intraday market gets a fundamental role allowing agents to adjust their position close to the delivery time. In this work we suggest an agent-based model of intraday market combined with genetics algorithms to understand what the best strategy could be adopted by players in order to optimize the market efficiency in terms of welfare and unsold quantity. In the first part we show the effect on the market prices of different scenarios in which players aim at maximizing their revenues and selling/buying all their volumes. In the second part we show the effect of a particular genetic algorithm on the model, focusing on how agents can adapt their strategy to enhance the market efficiency. Comparative analyses are also performed to investigate how the welfare of the system increases as well as the unsold quantity decrease when genetic algorithm is introduced
Keywords
continuous intraday market; agent-based model; genetics algorithm; power system
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.