Multi-Agent System For Competitive Electricity Markets Research Articles

MASCEM: Optimizing the performance of a multi-agent system

The electricity market sector has suffered massive changes in the last few decades. The worldwide electricity market restructuring has been conducted to potentiate the increase in competitiveness and thus decrease electricity prices. However, the complexity in this sector has grown significantly as well, with the emergence of several new types of players, interacting in a constantly changing environment. Several electricity market simulators have been introduced in recent years with the purpose of supporting operators, regulators, and the involved players in understanding and dealing with this complex environment. This paper presents a new, enhanced version of MASCEM (Multi-Agent System for Competitive Electricity Markets), an electricity market simulator with over ten years of existence, which had to be restructured in order to be able to face the highly demanding requirements that the decision support in this field requires. This restructuring optimizes the performance of MASCEM, both in results and execution time.

Support Vector Machines for decision support in electricity markets׳ strategic bidding

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors׳ research group has developed a multi-agent system: Multi-Agent System for Competitive Electricity Markets (MASCEM), which simulates the electricity markets environment. MASCEM is integrated with Adaptive Learning Strategic Bidding System (ALBidS) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. This paper presents the application of a Support Vector Machines (SVM) based approach to provide decision support to electricity market players. This strategy is tested and validated by being included in ALBidS and then compared with the application of an Artificial Neural Network (ANN), originating promising results: an effective electricity market price forecast in a fast execution time. The proposed approach is tested and validated using real electricity markets data from MIBEL – Iberian market operator.

Adaptive learning in agents behaviour: A framework for electricity markets simulation

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent elect

Strategic bidding in electricity markets: An agent-based simulator with game theory for scenario analysis

Electricity markets are complex environments, involving a large number of different entities, with specific characteristics and objectives, making their decisions and interacting in a dynamic scene. Game-theory has been widely used to support decisions in competitive environments; therefore its app lication in electricity markets can prove to be a high potential tool. This paper proposes a new scenario analysis algorithm, which includes the application of game-theory, to evaluate and preview different scenarios and provide players with the ability to strategically react in order to exhibit the behavior that better fits their objectives. This model includes forecasts of competitor players' actions, to build models of their behavior, in order to define the most probable expected scenarios. Once the scenarios are defined, game theory is applied to support the choice of the action to be performed. Our use of game theory is intended for supporting one specific agent and not for achieving the equilibrium in the market. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. The scenario analysis algorithm has been tested within MASCEM and our experimental findings with a case study based on real data from the Iberian Electricity Market are presented and discussed.