Quasi-dynamic load and battery sizing and scheduling for stand-alone solar system using mixed-integer linear programming

Considering the intermittency of renewable energy systems, a sizing and scheduling model is proposed for a finite number of static electric loads. The model objective is to maximize solar energy utilization with and without storage. For the application of optimal load size selection, the energy production of a solar photovoltaic is assumed to be consumed by a finite number of discrete loads in an off-grid system using mixed-integer linear programming. Additional constraints are battery charge and discharge limitations and minimum uptime and downtime for each unit. For a certain solar power profile the model outputs optimal unit size as well as the optimal scheduling for both units and battery charge and discharge (if applicable). The impact of different solar power profiles and minimum up and down time constraints on the optimal unit and battery sizes are studied. The battery size required to achieve full solar energy utilization decreases with the number of units and with increased flexibility of the units (shorter on and off-time). A novel formulation is introduced to model quasi-dynamic units that gradually start and stop and the quasi-dynamic units increase solar energy utilization. The model can also be applied to search for the optimal number of units for a given cost function.

[1]  Y. H. Wan,et al.  Long-Term Wind Power Variability , 2012 .

[2]  Vicenç Puig,et al.  Two-layer scheduling scheme for pump stations , 2014, 2014 IEEE Conference on Control Applications (CCA).

[3]  S. Nash,et al.  Linear and Nonlinear Optimization , 2008 .

[4]  Derek Abbott,et al.  Addressing the Intermittency Challenge: Massive Energy Storage in a Sustainable Future [Scanning the Issue] , 2012, Proc. IEEE.

[5]  Raymond A. de Callafon,et al.  Optimal switchable load sizing and scheduling for standalone renewable energy systems , 2017, 1702.00870.

[6]  Miguel Ángel Egido,et al.  The sizing of stand alone PV-system: A review and a proposed new method , 1992 .

[7]  Peter Palensky,et al.  Model based predictive control for a solar-thermal system , 2011, IEEE Africon '11.

[8]  S. Pelland,et al.  Estimating the uncertainty in long-term photovoltaic yield predictions , 2013 .

[9]  Raymond A. de Callafon,et al.  Model predictive load scheduling using solar power forecasting , 2016, 2016 American Control Conference (ACC).

[10]  Raymond A. de Callafon,et al.  Reliability of dynamic load scheduling with solar forecast scenarios , 2016, 2016 Annual IEEE Systems Conference (SysCon).

[11]  Jan Kleissl,et al.  Solar Desalination System Model for Sizing of Photovoltaic Reverse Osmosis (PVRO) , 2015 .

[12]  José M. N. Vieira,et al.  Implementation of a stand-alone photovoltaic lighting system with MPPT battery charging and LED current control , 2010, 2010 IEEE International Conference on Control Applications.

[13]  D. E. R E K A B B O T T.  Addressing the Intermittency Challenge : Massive Energy Storage in a Sustainable Future , 2012 .

[14]  Zheren Ma,et al.  Optimal power dispatch and control of a wind turbine and battery hybrid system , 2015, 2015 American Control Conference (ACC).

[15]  Shaolei Ren,et al.  Bidirectional Energy Trading and Residential Load Scheduling with Electric Vehicles in the Smart Grid , 2013, IEEE Journal on Selected Areas in Communications.

[16]  R. Hanna,et al.  Impact Research of High Photovoltaics Penetration Using High Resolution Resource Assessment with Sky Imager and Power System Simulation , 2022 .

[17]  Andrey V. Savkin,et al.  Optimal size of battery energy storage and monotonic charging/discharging strategies for wind farms , 2014, 2014 IEEE Conference on Control Applications (CCA).

[18]  Giri Venkataramanan,et al.  Generation unit sizing and cost analysis for stand-alone wind, photovoltaic, and hybrid wind/PV systems , 1998 .