Demonstration of ALBidS: Adaptive Learning Strategic Bidding System

• Main Author: Pinto, Tiago
• Publication Date: 2016
• Other Authors: Vale, Zita, Praça, Isabel, Santos, Gabriel
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: http://hdl.handle.net/10400.22/17342
• topic: Electricity Market; Realistic Simulation Conditions; Global State Graph; Context Awareness Capabilities; Require Decision Support

Current worldwide electricity markets are strongly affected by the increasing use of renewable energy sources [1]. This increase has been stimulated by new energy policies that result from the growing concerns regarding the scarcity of fossil fuels and their impact in the environment. This has also led to an unavoidable restructuring of the power and energy sector, which was forced to adapt to the new paradigm [2]. The restructuring process resulted in a deep change in the operation of competitive electricity markets. The restructuring made the market more competitive, but also more complex, placing new challenges to the participants, which increases the difficulty of decision making. This is exacerbated by the increasing number of new market types that are being implemented to deal with the new challenges. Therefore, the intervenient entities are relentlessly forced to rethink their behaviour and market strategies in order to cope with such a constantly changing environment [2].