Mitigating market risk for wind power providers via financial risk exchange

When wind power producers (WPPs) participate in forward electricity markets, they become exposed to real-time (RT) market risks from uncertain generation outputs and highly volatile RT market prices. This joint volume-price risk causes a risk-averse WPP to sell less energy than the expected generation, which discourages the WPP from fully enjoying the benefits of participating in forward electricity markets. In order to mitigate volume-price risks from the RT market, this paper proposes a financial instrument referred to as a risk exchange (REX) that enables the WPPs to trade random net payments from uncertain prices and generation outputs, after the day-ahead market is cleared. A negotiation for the REX is modeled by a bargaining game based on a conflict of interest in determining the REX amounts. Both Nash and Rubinstein's bargaining game models are addressed to analyze the REX bargaining game. It is shown that there is a unique outcome of the game which can be achieved by using a pure strategy. Moreover, a central planner who aims to minimize the aggregated risks of the WPPs is explored. Numerical examples demonstrate that the REX is able to reduce RTM risks successfully and encourages the WPPs to sell more energy to the DAM. Since the REX is not limited by physical constraints in power systems, it can be traded by the WPPs exposed to different locational marginal prices (LMPs).

[1]  Chi-Keung Woo,et al.  The impact of wind generation on the electricity spot-market price level and variance: The Texas experience , 2011 .

[2]  Chi-Keung Woo,et al.  Virtual Bidding, Wind Generation and California's Day-Ahead Electricity Forward Premium , 2015 .

[3]  G. Strbac,et al.  Trading Wind Generation in Short-Term Energy Markets , 2002, IEEE Power Engineering Review.

[4]  R. Wiser,et al.  2014 Wind Technologies Market Report , 2015 .

[5]  James E. Payne,et al.  Day-Ahead Premiums on the Midwest ISO , 2009 .

[6]  F. Longstaff,et al.  Electricity Forward Prices: A High-Frequency Empirical Analysis , 2002 .

[7]  Duehee Lee,et al.  An Offer Strategy for Wind Power Producers That Considers the Correlation Between Wind Power and Real-Time Electricity Prices , 2018, IEEE Transactions on Sustainable Energy.

[8]  K. Poolla,et al.  Coalitional Aggregation of Wind Power , 2013, IEEE Transactions on Power Systems.

[9]  A. Rubinstein Perfect Equilibrium in a Bargaining Model , 1982 .

[10]  J. Nash THE BARGAINING PROBLEM , 1950, Classics in Game Theory.

[11]  Benjamin F. Hobbs,et al.  The Design of US Wholesale Energy and Ancillary Service Auction Markets: Theory and Practice , 2008 .

[12]  Janina Ketterer,et al.  The impact of wind power generation on the electricity price in Germany , 2014 .

[13]  M. El-Sharkawi,et al.  Coordinated Trading of Wind and Thermal Energy , 2011, IEEE Transactions on Sustainable Energy.

[14]  Erin Mastrangelo,et al.  Financial Arbitrage and Efficient Dispatch in Wholesale Electricity Markets , 2015 .

[15]  H. Farahmand,et al.  Balancing Market Integration in the Northern European Continent: A 2030 Case Study , 2012, IEEE Transactions on Sustainable Energy.

[16]  B. F. Hobbs,et al.  Opportunity Cost Bidding by Wind Generators in Forward Markets: Analytical Results , 2011, IEEE Transactions on Power Systems.

[17]  Shahram Jadid,et al.  The role of demand response in single and multi-objective wind-thermal generation scheduling: A stochastic programming , 2014 .

[18]  H. Vincent Poor,et al.  Wind Aggregation Via Risky Power Markets , 2015, IEEE Transactions on Power Systems.

[19]  R. Rockafellar,et al.  Deviation Measures in Risk Analysis and Optimization , 2002 .

[20]  A. Daraeepour,et al.  A new self-scheduling strategy for integrated operation of wind and pumped-storage power plants in power markets , 2011 .

[21]  A. Rubinstein,et al.  Bargaining and Markets , 1991 .

[22]  Daniel R. Vincent,et al.  The Declining Price Anomaly , 1993 .

[23]  Antonio J. Conejo,et al.  Managing the financial risks of electricity producers using options , 2012 .

[24]  Antonio J. Conejo,et al.  Short-Term Trading for a Wind Power Producer , 2010 .

[25]  Ram Rajagopal,et al.  Competition and Coalition Formation of Renewable Power Producers , 2015, IEEE Transactions on Power Systems.

[26]  Mar Reguant,et al.  Sequential Markets, Market Power and Arbitrage , 2014 .

[27]  P. Varaiya,et al.  Bringing Wind Energy to Market , 2012, IEEE Transactions on Power Systems.

[28]  Christopher R. Knittel,et al.  Inefficiencies and Market Power in Financial Arbitrage: A Study of California's Electricity Markets , 2004 .

[29]  George Kariniotakis,et al.  Risk-based strategies for wind/pumped-hydro coordination under electricity markets , 2009, 2009 IEEE Bucharest PowerTech.

[30]  Ned Djilali,et al.  Modeling framework and validation of a smart grid and demand response system for wind power integration , 2014 .

[31]  V. Mendes,et al.  A risk-averse optimization model for trading wind energy in a market environment under uncertainty , 2011 .

[32]  Day-Ahead Premiums on the New England ISO , 2008 .

[33]  Simon Porcher,et al.  Hedging strategies in energy markets: the case of electricity retailers , 2015 .

[34]  Philippe Artzner,et al.  Coherent Measures of Risk , 1999 .

[35]  Joao Catalao,et al.  Risk-Constrained Offering Strategy of Wind Power Producers Considering Intraday Demand Response Exchange , 2014, IEEE Transactions on Sustainable Energy.

[36]  R. Baldick,et al.  Computing the Electricity Market Equilibrium: Uses of market equilibrium models , 2006, 2006 IEEE PES Power Systems Conference and Exposition.

[37]  Casimir Lorenz,et al.  New Cross-Border Electricity Balancing Arrangements in Europe , 2014 .

[38]  Henrik Madsen,et al.  A bilevel model for electricity retailers' participation in a demand response market environment , 2013 .

[39]  Kameshwar Poolla,et al.  Wind energy aggregation: A coalitional game approach , 2011, IEEE Conference on Decision and Control and European Control Conference.

[40]  P. Pinson,et al.  Trading Wind Generation From Short-Term Probabilistic Forecasts of Wind Power , 2007, IEEE Transactions on Power Systems.