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

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.

[1]  R D Zimmerman,et al.  MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education , 2011, IEEE Transactions on Power Systems.

[2]  J. Contreras,et al.  ARIMA models to predict next-day electricity prices , 2002 .

[3]  Sakshi Pahwa,et al.  Abruptness of Cascade Failures in Power Grids , 2014, Scientific Reports.

[4]  A. David,et al.  Strategic bidding in competitive electricity markets: a literature survey , 2000, 2000 Power Engineering Society Summer Meeting (Cat. No.00CH37134).

[5]  F. Bouffard,et al.  Stochastic security for operations planning with significant wind power generation , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[6]  J. Contreras,et al.  ARIMA Models to Predict Next-Day Electricity Prices , 2002, IEEE Power Engineering Review.

[7]  M. O'Malley,et al.  A new approach to quantify reserve demand in systems with significant installed wind capacity , 2005, IEEE Transactions on Power Systems.

[8]  Haili Song,et al.  Nash Equilibrium Bidding Strategies in a Bilateral Electricity Market , 2002, IEEE Power Engineering Review.

[9]  J. Contreras,et al.  Forecasting Next-Day Electricity Prices by Time Series Models , 2002, IEEE Power Engineering Review.

[10]  A. Conejo,et al.  Short-Term Trading for a Wind Power Producer , 2010, IEEE Transactions on Power Systems.

[11]  R. Schroeder LITERATURE SURVEY , 1981 .

[12]  Ehab F. El-Saadany,et al.  DG allocation for benefit maximization in distribution networks , 2013, IEEE Transactions on Power Systems.

[13]  F. Gaoa,et al.  Electricity market equilibrium model with resource constraint and transmission congestion , 2016 .

[14]  M. Klobasa Analysis of demand response and wind integration in Germany's electricity market , 2010 .

[15]  Gerald B. Sheblé,et al.  Electricity market equilibrium model with resource constraint and transmission congestion , 2010 .

[16]  Mridul Pentapalli,et al.  A comparative study of Roth-Erev and modified Roth-Erev reinforcement learning algorithms for uniform-price double auctions , 2008 .

[17]  Derek W. Bunn,et al.  Agent-based simulation-an application to the new electricity trading arrangements of England and Wales , 2001, IEEE Trans. Evol. Comput..

[18]  S. M. Moghaddas-Tafreshi,et al.  Bidding Strategy of Virtual Power Plant for Participating in Energy and Spinning Reserve Markets—Part II: Numerical Analysis , 2011, IEEE Transactions on Power Systems.

[19]  Mohammad Ali Rastegar,et al.  Agent-based model of the Italian wholesale electricity market , 2009, 2009 6th International Conference on the European Energy Market.

[20]  F. Galiana,et al.  Stochastic Security for Operations Planning With Significant Wind Power Generation , 2008, IEEE Transactions on Power Systems.

[21]  E. Allen,et al.  Reserve markets for power systems reliability , 2000 .

[22]  Fangxing Li,et al.  A Probability-Driven Multilayer Framework for Scheduling Intermittent Renewable Energy , 2012, IEEE Transactions on Sustainable Energy.

[23]  G. Joos,et al.  The potential of distributed generation to provide ancillary services , 2000, 2000 Power Engineering Society Summer Meeting (Cat. No.00CH37134).

[24]  Guido Caldarelli,et al.  Distributed Generation and Resilience in Power Grids , 2012, CRITIS.

[25]  Haili Song,et al.  Optimal electricity supply bidding by Markov decision process , 2000 .

[26]  Leigh Tesfatsion,et al.  Market power and efficiency in a computational electricity market with discriminatory double-auction pricing , 2001, IEEE Trans. Evol. Comput..

[27]  S. M. Moghaddas-Tafreshi,et al.  Bidding Strategy of Virtual Power Plant for Participating in Energy and Spinning Reserve Markets—Part I: Problem Formulation , 2011, IEEE Transactions on Power Systems.

[28]  P. Luh,et al.  Selecting input factors for clusters of Gaussian radial basis function networks to improve market clearing price prediction , 2003 .

[29]  Madeleine Gibescu,et al.  Short-Term Energy Balancing With Increasing Levels of Wind Energy , 2012, IEEE Transactions on Sustainable Energy.

[30]  John Lygeros,et al.  A Probabilistic Framework for Reserve Scheduling and ${\rm N}-1$ Security Assessment of Systems With High Wind Power Penetration , 2013, IEEE Transactions on Power Systems.

[31]  A. Conejo,et al.  Market-clearing with stochastic security-part I: formulation , 2005, IEEE Transactions on Power Systems.

[32]  Manfred Gilli,et al.  Understanding complex systems , 1981, Autom..

[33]  Antonio Scala,et al.  Networks of Networks: The Last Frontier of Complexity , 2014 .