Short-Term trading for a photovoltaic power producer in electricity markets

Premiums for renewable energy are being reduced as a consequence of the world economic crisis. This paper models the trading of the energy generated by a photovoltaic generator. The problem is solved through stochastic mixed integer linear programming where the objective function aims at maximizing the profit of selling the photovoltaic production in the day-ahead market. The model is tested without any premium and market and imbalance market prices are forecasted using AR, MA and ARIMA models while photovoltaic production is simulated using Montecarlo method. The model is tested for a case study where the behaviour of the offer, imbalances, incomes and costs is analyzed.

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

[2]  Giampaolo Manfrida,et al.  Seawater pumping as an electricity storage solution for photovoltaic energy systems , 2014 .

[3]  Diego J. Pedregal,et al.  ECOTOOL: A general MATLAB Forecasting Toolbox with Applications to Electricity Markets , 2012 .

[4]  Ryan Wiser,et al.  Customer-economics of residential photovoltaic systems (Part 1): The impact of high renewable energy penetrations on electricity bill savings with net metering , 2014 .

[5]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[6]  L. Shi,et al.  Bidding strategy of microgrid with consideration of uncertainty for participating in power market , 2014 .

[7]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[8]  E.A. DeMeo,et al.  Accommodating wind's natural behavior , 2007, IEEE Power and Energy Magazine.

[9]  I. Erlich,et al.  Strategies for Wind Power Trading in Competitive Electricity Markets , 2008, IEEE Transactions on Energy Conversion.

[10]  Steven R. Weller,et al.  An optimization-based approach to scheduling residential battery storage with solar PV: Assessing customer benefit , 2015 .

[11]  Ruggero Schleicher-Tappeser,et al.  How renewables will change electricity markets in the next five years , 2012 .

[12]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .