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Project
Strategic Behaviour in Power Wholesale Electricity Markets: Design, Implementation & Validation of an Agent-based Simulation Platform
Liberalising the European electricity industry did not naturally produce its intended results. Network constraints, few dominant sellers in a relatively small market, complex market designs, price-inelastic consumers, reductions in generation capacity, unavailability of perfect information provided in real-time, and portfolio economics and technical characteristics induced the observed strategic gaming behaviour of generators.
In order to understand the evolution of the electricity market, dynamic market modelling tools can be applied. Using such models, all stakeholders can gain insights on the sensitivity of market design parameters against potential disturbances or market imperfections, and take necessary actions to pro-actively address them. How the state of an interconnected electrical system evolves after clearing the day-ahead market as organised under the European Power Exchange model, subject to strategic gaming behaviour has been studied. Presented contributions revolve around two research domains.
Firstly, a novel profit risk hedging offering strategy is presented. It submits the coordinated dispatch scheduleof thermal, hydropower and renewable power plants to the market operator. The generator pursues a total profit-maximising objective by simultaneously exercising physical and economic withholding while explicitly taking into account underlying technical constraints and plant economics. Price-responsive demand is realistically modelled by step-wise decreasingcurves. The consideration of portfolio flexibility to mitigate profit risks is proven to yield higher total profit than alternative strategies.
Secondly, the offering strategy is integrated in a newly designed dynamic electricity market model. Using multi-agent systems, each generator updates its perception of the market environment by evaluating theperformance of historic decisions on its profit. Four learning and decision processes have been designed. The first determines the optimal renewable energy supply quantity to submit with hydropower as reserve, in order to minimise future self-balancing responsibilities. The second determines whether to behave competitively or strategically. The third determines the degree to which the generator can strategically increase its profit. The last accounts for crossborder exchanges.
Results obtained by applying the model to case studies illustrate its validity. Consequently, by explicitly taking into account the most relevant market design parameters, the agent-based simulation platform is capable of answering research questions existing electricity market simulation tools cannotaddress.
In order to understand the evolution of the electricity market, dynamic market modelling tools can be applied. Using such models, all stakeholders can gain insights on the sensitivity of market design parameters against potential disturbances or market imperfections, and take necessary actions to pro-actively address them. How the state of an interconnected electrical system evolves after clearing the day-ahead market as organised under the European Power Exchange model, subject to strategic gaming behaviour has been studied. Presented contributions revolve around two research domains.
Firstly, a novel profit risk hedging offering strategy is presented. It submits the coordinated dispatch scheduleof thermal, hydropower and renewable power plants to the market operator. The generator pursues a total profit-maximising objective by simultaneously exercising physical and economic withholding while explicitly taking into account underlying technical constraints and plant economics. Price-responsive demand is realistically modelled by step-wise decreasingcurves. The consideration of portfolio flexibility to mitigate profit risks is proven to yield higher total profit than alternative strategies.
Secondly, the offering strategy is integrated in a newly designed dynamic electricity market model. Using multi-agent systems, each generator updates its perception of the market environment by evaluating theperformance of historic decisions on its profit. Four learning and decision processes have been designed. The first determines the optimal renewable energy supply quantity to submit with hydropower as reserve, in order to minimise future self-balancing responsibilities. The second determines whether to behave competitively or strategically. The third determines the degree to which the generator can strategically increase its profit. The last accounts for crossborder exchanges.
Results obtained by applying the model to case studies illustrate its validity. Consequently, by explicitly taking into account the most relevant market design parameters, the agent-based simulation platform is capable of answering research questions existing electricity market simulation tools cannotaddress.
Date:1 Oct 2009 → 24 Jan 2014
Keywords:Market mechanisms, electricity market, electric power market, agent-based simulation, agent-based modelling.
Disciplines:Modelling, Multimedia processing
Project type:PhD project