Abstract

Abstract

Abstract

Abstract

Abstract
The energy sector restructuring process in industrialized countries had the aim of reducing electricity prices by increasing competitiveness, and facilitate the integration of distributed energy resources. However, the complexity in market players' interactions has increased, and new problems have emerged. Decision support tools that facilitate the study and comprehension of these markets became extremely useful, providing players with competitive advantage. MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) arises in this context, modeling and simulating real electricity markets. It is crucial to MASCEM to have the ability to simulate as many market models and player types as possible, thus enhancing the ability to recreate the electricity markets reality in its maximum possible extent. This paper presents the EPEX Spot Day-Ahead market integration in MASCEM. EPEX Spot SE's mission is to lead European markets coupling in a single unified market, thus being crucial for the study of competitive electricity markets. © 2015 IEEE.

Abstract
The continuous changes in electricity markets' mechanisms and operations turn this environment into a challenging domain for the participating entities. Simulation tools are increasingly being used for decision support purposes of such entities. In particular, multi-agent based simulation, which facilitates the modeling of different types of mechanisms and players, is being fruitfully applied to the study of worldwide electricity markets. An effective decision support to market players' negotiations is, however, still not properly reached due to the uncertainty that results from the increasing penetration of renewable generation and the complexity of market mechanisms themselves. In this scope, this paper proposes a novel metalearner that provides decision support to market players in their negotiations. The proposed metalearner uses as input the output of several other market negotiation strategies, which are used to create a new, enhanced response. The final result is achieved through the combination and evolution of the strategies' learning results by applying a genetic algorithm. © 2015 IEEE.