Multi-agent Electricity Markets and Smart Grids Simulation with Connection to Real Physical Resources

The increasing penetration of distributed energy sources, mainly based on renewable generation, calls for an urgent emergence of novel advanced methods to deal with the associated problems. The consensus behind smart grids (SGs) as one of the most promising solutions for the massive integration of renewable energy sources in power systems has led to the development of several prototypes that aim at testing and validating SG methodologies. The urgent need to accommodate such resources require alternative solutions. This chapter presents a multi-agent based SG simulation platform connected to physical resources, so that realistic scenarios can be simulated. The SG simulator is also connected to the Multi-Agent Simulator of Competitive Electricity Markets, which provides a solid framework for the simulation of electricity markets. The cooperation between the two simulation platforms provides huge studying opportunities under different perspectives, resulting in an important contribution to the fields of transactive energy, electricity markets, and SGs. A case study is presented, showing the potentialities for interaction between players of the two ecosystems: a SG operator, which manages the internal resources of a SG, is able to participate in electricity market negotiations to trade the necessary amounts of power to fulfill the needs of SG consumers.This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement N. 641794 (project DREAM-GO). It has also received FEDER Funds through the COMPETE program and National Funds through FCT under the project UID/EEA/00760/2013. The authors gratefully acknowledge the valuable contribution of Bruno Canizes, Daniel Paiva, Gabriel Santos and Marco Silva to the work presented in the chapter.info:eu-repo/semantics/publishedVersio

Analysing the Impact of Rationality on the Italian Electricity Market

International audienceWe analyze the behavior of the Italian electricity market with an agent-based model. In particular, we are interested in testing the assumption that the market participants are fully rational in the economical sense. To this aim, we extend a previous model by considering a wider class of cases. After checking that the new model is a correct generalization of the existing model, we compare three optimization methods to implement the agents rationality and we verify that the model exhibits a very good fit to the real data. This leads us to conclude that our model can be used to predict the behavior of this market

Electric Vehicles in Imperfect Electricity Markets: A German Case Study

We analyze the impacts of a hypothetical fleet of plug-in electric vehicles on the imperfectly competitive German electricity market with a game-theoretic model. Electric vehicles bring both additional demand and additional storage capacity to the market. We determine their effects on prices, welfare, and electricity generation for various cases with different players being in charge of vehicle operations. We find that vehicle loading increases generator profits, but decreases consumer surplus. If excess vehicle batteries can be used for storage, welfare results are reversed: generating firms suffer from the price-smoothing effect of additional storage, whereas consumers benefit despite increasing overall demand. Results however depend on the player being in charge of storage operations, and on battery degradation costs. Strategic players tend to underutilize the storage capacity of the vehicle fleet, which may have negative welfare implications. In contrast, we find a small market power mitigating effect of electric vehicle recharging on oligopolistic generators. Overall, electric vehicles are unlikely to be a relevant source of market power in Germany

Noisy Data and Uncertain Coefficients: A Reply

In his comment on our paper, John H. Herbert brings out an important issue: [N]o one (except KSS, as far as I know) has recommended using the bonds approach for forecasting. KSS however, does not reply on a well-founded statistical basis for their forecasting procedure: it is decidedly ad hoc. We are pleased that Herbert has recognized the potential of "the bounds approach for forecasting" as a viable alternative for forecasting. His major concern is with the "ad hoc" nature of our forecasting procedure. This apparent occurs because forecasts generated by the NVM do not come with confidence intervals similar to those placed on forecasts generated by classical regression techniques. We wish to reply.