Participation of photovoltaic power producers in short-term electricity markets based on rescheduling and risk-hedging mapping

Abstract Optimal bidding that considers different electricity market floors can increase the financial gains of photovoltaic (PV) power producers. However, the current approach to trading PV power essentially consists of committing to sell the forecasted PV generation. To analyze profits and investigate new business opportunities for PV power producers, this paper proposes two novel stochastic programming-based methods for scheduling and rescheduling for trading the PV generated energy in day-ahead and intraday electricity markets. Risk-hedging is also considered in terms of co-optimizing the expected profit with the Conditional Value-at-Risk (CVaR) metric. As a consequence of the structure and organization of the market floors and due to different market windows, rescheduling is necessary to exploit the most recent information. Updated rescheduling progressively reveals actual profits or losses, risk-hedging possible engagement in business transactions, and the final effect of strategic bidding. A case study in the Spanish electricity market based on actual data is presented. The analysis of the case study shows the influence of the three market floors (day-ahead, intraday, and imbalance), the participation in multiple intraday sessions, risk-hedging, and rescheduling on the profits of the PV producer.

[1]  Johanna Cludius,et al.  The merit order effect of wind and photovoltaic electricity generation in Germany 2008–2016: Estimation and distributional implications , 2014 .

[2]  Zita Vale,et al.  Reschedule of Distributed Energy Resources by an Aggregator for Market Participation , 2018 .

[3]  Xinghuo Yu,et al.  Optimizing rooftop photovoltaic distributed generation with battery storage for peer-to-peer energy trading , 2018, Applied Energy.

[4]  José L. Bernal-Agustín,et al.  Optimal investment portfolio in renewable energy: The Spanish case , 2009 .

[5]  Anthony Papavasiliou,et al.  Adaptive Trading in Continuous Intraday Electricity Markets for a Storage Unit , 2020, IEEE Transactions on Power Systems.

[6]  Filipe Joel Soares,et al.  Optimal bidding strategy for an aggregator of prosumers in energy and secondary reserve markets , 2019, Applied Energy.

[8]  Zechun Hu,et al.  Rolling Optimization of Wind Farm and Energy Storage System in Electricity Markets , 2015, IEEE Transactions on Power Systems.

[9]  J. Catalão,et al.  Optimal Single Wind Hydro-Pump Storage Bidding in Day-Ahead Markets Including Bilateral Contracts , 2016, IEEE Transactions on Sustainable Energy.

[10]  Stefan Spinler,et al.  Risk hedging via options contracts for physical delivery , 2003, OR Spectr..

[11]  J. I. Muñoz,et al.  Optimal coordinated wind-hydro bidding strategies in day-ahead markets , 2013, IEEE Transactions on Power Systems.

[12]  Lieven Vandevelde,et al.  Solar Commercial Virtual Power Plant , 2013, 2013 IEEE Power & Energy Society General Meeting.

[13]  João F. D. Rodrigues,et al.  Analysis of feed-in tariff policies for solar photovoltaic in China 2011–2016 , 2017 .

[14]  Juan M. Morales,et al.  Intraday Trading of Wind Energy , 2015, IEEE Transactions on Power Systems.

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

[16]  Wei Qiao,et al.  Risk-averse Offer Strategy of a Photovoltaic Solar Power Plant with Virtual Bidding in Electricity Markets , 2019, 2019 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT).

[17]  Joao P. S. Catalao,et al.  Optimal coordinated wind-photovoltaic bidding in electricity markets , 2015, 2015 Australasian Universities Power Engineering Conference (AUPEC).

[18]  A. Conejo,et al.  Decision making under uncertainty in electricity markets , 2010, 2006 IEEE Power Engineering Society General Meeting.

[19]  P. Rodriguez,et al.  Predictive Power Control for PV Plants With Energy Storage , 2013, IEEE Transactions on Sustainable Energy.

[20]  Mike Sandiford,et al.  Retrospective modeling of the merit-order effect on wholesale electricity prices from distributed photovoltaic generation in the Australian National Electricity Market , 2013 .

[21]  Åsa Grytli Tveten,et al.  Solar feed-in tariffs and the merit order effect: A study of the German electricity market , 2013 .

[22]  Pavol Bauer,et al.  System design for a solar powered electric vehicle charging station for workplaces , 2016 .

[23]  Robert Margolis,et al.  Exploring the market for third-party-owned residential photovoltaic systems: insights from lease and power-purchase agreement contract structures and costs in California , 2015 .

[24]  Md. Noor-E-Alam,et al.  A machine learning based stochastic optimization framework for a wind and storage power plant participating in energy pool market , 2018, Applied Energy.

[25]  Aitor Milo,et al.  Annual Optimized Bidding and Operation Strategy in Energy and Secondary Reserve Markets for Solar Plants With Storage Systems , 2019, IEEE Transactions on Power Systems.

[26]  P. Pinson,et al.  Trading wind power through physically settled options and short‐term electricity markets , 2019, Wind Energy.

[27]  Guzmán Díaz,et al.  Optimal operation value of combined wind power and energy storage in multi-stage electricity markets , 2019, Applied Energy.

[28]  Joao P. S. Catalao,et al.  Optimal Wind Reversible Hydro Offering Strategies for Midterm Planning , 2015, IEEE Transactions on Sustainable Energy.

[29]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[30]  Benjamin C. McLellan,et al.  Substitution effect of renewable portfolio standards and renewable energy certificate trading for feed-in tariff , 2017, Applied Energy.

[31]  Nadarajah Mithulananthan,et al.  Strategic allocation of community energy storage in a residential system with rooftop PV units , 2017 .

[32]  Lion Hirth,et al.  Short-term electricity trading for system balancing: An empirical analysis of the role of intraday trading in balancing Germany's electricity system , 2019, Renewable and Sustainable Energy Reviews.

[33]  Anshuman Sahoo,et al.  The road ahead for solar PV power , 2018, Renewable and Sustainable Energy Reviews.

[34]  Chiung-Wen Hsu,et al.  Using a system dynamics model to assess the effects of capital subsidies and feed-in tariffs on solar PV installations , 2012 .

[35]  Pierre Audinet,et al.  The price of solar energy: Comparing competitive auctions for utility-scale solar PV in developing countries , 2018, Energy Policy.

[36]  Jussi Nikkinen,et al.  Market specific seasonal trading behavior in NASDAQ OMX electricity options , 2019, Journal of Commodity Markets.

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

[38]  Joao P. S. Catalao,et al.  ANN-based scenario generation methodology for stochastic variables of electric power systems , 2016 .

[39]  Josep M. Guerrero,et al.  A model predictive control strategy of PV-Battery microgrid under variable power generations and load conditions , 2018, Applied Energy.

[40]  Joao P. S. Catalao,et al.  Short-Term trading for a photovoltaic power producer in electricity markets , 2015, 2015 IEEE Power & Energy Society General Meeting.

[41]  Shmuel S. Oren,et al.  Hedging quantity risks with standard power options in a competitive wholesale electricity market , 2006 .