Agent-based model of the Italian wholesale electricity market

Rastegar, Mohammad Ali; Guerci, Éric; Cincotti, Silvano

doi:10.1109/eem.2009.5207128

Cited by 15 publications

(10 citation statements)

References 12 publications

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“…We base our agent model on the Roth-Erev algorithm [28]; such kind of algorithms have already been applied for simulating the Italian ODA electricity market [29]. In such kind of models, agents learn how to place optimal bids in competitive auctions with the aim of buying (or selling) in the most convenient way.…”

Section: Agent Based Simulation Of Electricity Balancing Marketmentioning

confidence: 99%

Green Power Grids: How Energy from Renewable Sources Affects Networks and Markets

et al. 2015

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The increasing attention to environmental issues is forcing the implementation of novel energy models based on renewable sources. This is fundamentally changing the configuration of energy management and is introducing new problems that are only partly understood. In particular, renewable energies introduce fluctuations which cause an increased request for conventional energy sources to balance energy requests at short notice. In order to develop an effective usage of low-carbon sources, such fluctuations must be understood and tamed. In this paper we present a microscopic model for the description and for the forecast of short time fluctuations related to renewable sources in order to estimate their effects on the electricity market. To account for the inter-dependencies in the energy market and the physical power dispatch network, we use a statistical mechanics approach to sample stochastic perturbations in the power system and an agent based approach for the prediction of the market players’ behavior. Our model is data-driven; it builds on one-day-ahead real market transactions in order to train agents’ behaviour and allows us to deduce the market share of different energy sources. We benchmarked our approach on the Italian market, finding a good accordance with real data.

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Section: Agent Based Simulation Of Electricity Balancing Marketmentioning

confidence: 99%

Green Power Grids: How Energy from Renewable Sources Affects Networks and Markets

et al. 2015

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“…Moreover, (Guerci et al, 2007) provided the first version of the Genoa Artificial Power Exchange and compared the discriminatory and the uniform price auction mechanism with heterogenous agents. Finally, (Rastegar et al, 2009) firstly attempted to create an agent-based model of the Italian Electricity Market, with a reduced transmission network grid and a simplified description of GenCos.…”

Section: Agent-based Modeling Of the Italian Electricity Day-ahead Mamentioning

confidence: 99%

“…In this paper, we present the Genoa Artificial Power Exchange (GAPEX), an agent-based framework for modeling and simulating electricity markets. In particular, the general GAPEX framework is presented that allows us to generalize the models and to overcome some limitations and simplifications that characterized preliminary version of the framework ((Cincotti et al, 2005), (Guerci et al, 2007) and (Rastegar et al, 2009)). In this paper, attention is devoted to model design and developing within GAPEX.…”

Section: Introductionmentioning

confidence: 99%

Gapex: An Agent-Based Framework for Power Exchange Modeling and Simulation

Cincotti

Gallo

2012

Proceedings of the 4th International Conference on Agents and Artificial Intelligence

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The paper presents an agent-based framework for modeling and simulating power exchanges, the Genoa Artificial Power Exchange (GAPEX). The framework is implemented in MATLAB using the OOP paradigm, which allows one to define classes using a Java/C++ like syntax. GAPEX allows creation of artificial power exchanges where what-if analysis can be performed. GAPEX also reproduces exactly the market clearing procedure (e.g. by calculating Locational Marginal Prices based on the Italian high-voltage transmission network with its zonal subdivision) and the generation plants modeled are in direct correspondence with the real ones. Moreover, the presence of affine total cost functions for the generation plants results in payoff either positive, negative and null. This has major implications as negative reward are not generally considered by reinforcement learning algorithms. In order to overcome such limitation, an enhanced version of the Roth-Erev algorithm (i.e., that takes into account also negative payoffs) is presented and discussed. Results point out effectiveness of the proposed enhanced learning algorithm. Moreover, computational experiments performed within GAPEX point out a close agreement with historical real market data during both peak-and off-peak load hours thus confirming the direct applicability of GAPEX to model and to simulate power exchanges.

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“…Variations on the Q-learning technique have been used to study congestion management schemes [31], combined electricity and gas markets [32] and emissions allowance trading [33]. The Roth-Erev method has been used to study market power [23], the Italian wholesale electricity market [34], cross-holdings 1 [35], and interrelationships between contracts markets and balancing markets [36], [37].…”

Section: Related Workmentioning

confidence: 99%

Comparing Policy Gradient and Value Function Based Reinforcement Learning Methods in Simulated Electrical Power Trade

Lincoln

Galloway

Stephen

et al. 2012

IEEE Trans. Power Syst.

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This version is available at https://strathprints.strath.ac.uk/33071/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. Abstract-In electrical power engineering, reinforcement learning algorithms can be used to model the strategies of electricity market participants. However, traditional value function based reinforcement learning algorithms suffer from convergence issues when used with value function approximators. Function approximation is required in this domain to capture the characteristics of the complex and continuous multivariate problem space. The contribution of this paper is the comparison of policy gradient reinforcement learning methods, using artificial neural networks for policy function approximation, with traditional value function based methods in simulations of electricity trade. The methods are compared using an AC optimal power flow based power exchange auction market model and a reference electric power system model.

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Agent-based model of the Italian wholesale electricity market

Cited by 15 publications

References 12 publications

Green Power Grids: How Energy from Renewable Sources Affects Networks and Markets

Green Power Grids: How Energy from Renewable Sources Affects Networks and Markets

Gapex: An Agent-Based Framework for Power Exchange Modeling and Simulation

Comparing Policy Gradient and Value Function Based Reinforcement Learning Methods in Simulated Electrical Power Trade

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