Cleaner power generation through market-driven generation expansion planning: an agent-based hybrid framework of game theory and Particle Swarm Optimization

• We proposed a two-sided agent-based framework that provides a suitable platform for sustainable GEP.
• We compared the results of the modified game model to the results of the proposed agent-based game theoretic model.
• The results indicated that incorporating the demand side interactions leads to a more economical and cleaner generation plan.

In power markets, the competition on both price and quantity can be used as a trigger towards development of a sustainable power sector furthermore; it can increase the use of renewable energy sources and enhance energy efficiency on the supply and demand sides. In this regard, it is required to develop a reliable decision support system for sustainable generation expansion planning under a good understanding of the aforementioned issues. Game theoretic models as decision support tools have recently received increasing attention from many researchers in this field; however, they assume the supplier entities make a long-term strategic plan with perfect foresight in a certain problem environment, without considering inter-temporal dynamics of market and effects of demand side interactions on generation expansion decisions. In this paper, we propose a two-side multi-agent based modeling framework which undertakes these tasks using a hybrid simulation approach of game theory and Particle Swarm Optimization (PSO). A Case Study of Iran's power system is used to illustrate the usefulness of the proposed planning approach and also to discuss its efficiency. The results showed that the proposed integrated approach provides not only an economical generation expansion plan but also a cleaner one compared to the game theoretic approach.