Intelligent photovoltaic power plants management strategy for market participation

Energy storage systems integration with large-scale PV power plants, named Intelligent PV power plant (IPV), could contribute to improve the viability of these plants and to provide ancillary services to the main grid. The number and extension of the provided services depend not only on the size of the storage system, but also on the manner how this storage system is managed. In this paper the market participation based on different firming control strategies of an IPV power plant is proposed to optimize the economic exploitation based on the storage system management considering PV generation predictions. The most appropriate firming control strategy is selected to participate on the daily market which is strengthened with an online model predictive control (MPC) to compensate the PV prediction errors participating in the intraday market. The real operation of the Iberian Peninsula market integration is also explained and developed. The development of this management strategy considers as a case study a real IPV plant located in Tudela (Navarre, Spain) and owned by Acciona Energía, which included a Lithium-ion based energy storage system from 2012 to 2013 in the framework of a European project.

[1]  Rodolfo Dufo López Dimensionado y control óptimos de sistemas híbridos aplicando algoritmos evolutivos , 2007 .

[2]  Y Riffonneau,et al.  Optimal Power Flow Management for Grid Connected PV Systems With Batteries , 2011, IEEE Transactions on Sustainable Energy.

[3]  L. Marroyo,et al.  Storage requirements for PV power ramp-rate control , 2014 .

[4]  Alberto Carbajo Josa Los mercados eléctricos y los servicios de ajuste del sistema , 2007 .

[5]  Catalin Gavriluta,et al.  Storage system requirements for grid supporting PV-power plants , 2014, 2014 IEEE Energy Conversion Congress and Exposition (ECCE).

[6]  Sonia Martínez,et al.  Storage Size Determination for Grid-Connected Photovoltaic Systems , 2011, IEEE Transactions on Sustainable Energy.

[7]  David Q. Mayne,et al.  Constrained model predictive control: Stability and optimality , 2000, Autom..

[8]  C. Amzallag,et al.  Standardization of the rainflow counting method for fatigue analysis , 1994 .

[9]  Ning Lu,et al.  A comparison of forecast error generators for modeling wind and load uncertainty , 2013, 2013 IEEE Power & Energy Society General Meeting.

[10]  Pedro Rodríguez,et al.  Evaluation of Storage Energy Requirements for Constant Production in PV Power Plants , 2013, IEEE Transactions on Industrial Electronics.

[11]  Victor Isaac Herrera,et al.  Optimal energy management of a hybrid electric bus with a battery-supercapacitor storage system using genetic algorithm , 2015, 2015 International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles (ESARS).

[12]  Eduardo F. Camacho,et al.  Commercial Model Predictive Control Schemes , 2007 .

[13]  Ion Etxeberria-Otadui,et al.  Enhanced experimental PV plant grid-integration with a MW Lithium-Ion energy storage system , 2013, 2013 IEEE Energy Conversion Congress and Exposition.

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

[15]  N. Aparicio,et al.  Daily Solar Energy Estimation for Minimizing Energy Storage Requirements in PV Power Plants , 2013, IEEE Transactions on Sustainable Energy.