Optimal Investment Strategies for Solar Energy Based Systems

Solar energy, as an inexhaustible renewable energy, can be used to produce heat and electricity. It is of great importance to examine the strategy for investment on solar energy technology. In response to varying electricity price in the electricity market, the battery energy storage system (BESS) can be used to get price arbitrage. This paper proposes an optimal configuration model for a photovoltaic (PV) system, solar heating system, and BESS in order to obtain maximum profit for investors. The investment potential of these systems is compared and analyzed based on return on investment (ROI) index which is defined to evaluate economic profitability. A bi-level programming is adopted to optimize the operation strategy of batteries (inner layer), the size of PV system and solar heating system, and the size of batteries (outer layer) including their maximum discharge/charge power and capacity. Sequential quadratic programming (SQP) method and particle swarm optimization (PSO) are used as optimization methods. In the case study, five investment strategies are investigated in order to decide how to invest in PV modules, batteries, and solar thermal collectors. The results show that the BESS may be a preferable choice for the investors if the investment cost of BESS goes down a lot in the future. Investing in solar energy for both heat and power may be not reasonable because the ROI of this strategy is always higher than either investing in heat or in power. The optimal strategy may be changed with the fluctuation of heat and electricity prices.

[1]  M. L. Baughman,et al.  Scheduling of cool storage using non-linear programming techniques , 1995 .

[2]  Samee U. Khan,et al.  Solar water heating systems and their market trends , 2013 .

[3]  Alan Henderson,et al.  Solar domestic hot water systems using latent heat energy storage medium: A review , 2015 .

[4]  Ahmad Atieh,et al.  Case study on the return on investment (ROI) for using renewable energy to power-up typical house in Saudi Arabia☆ , 2015 .

[5]  Abdel-Karim Daud,et al.  Design of isolated hybrid systems minimizing costs and pollutant emissions , 2012 .

[6]  Dezso Sera,et al.  Investigation of wind speed cooling effect on PV panels in windy locations , 2016 .

[7]  Hesham A. Hefny,et al.  LDWMeanPSO: A new improved particle swarm optimization technique , 2011, 2011 Seventh International Computer Engineering Conference (ICENCO'2011).

[8]  Ximing Liang,et al.  Sequential Quadratic Programming Based on IPM for Constrained Nonlinear Programming , 2008, 2008 Eighth International Conference on Intelligent Systems Design and Applications.

[9]  Wei-Min Hu Operation of Modern Distribution Power Systems in Competitive Electricity Markets , 2012 .

[10]  V. Bebarta,et al.  316 , 2006, Annals of Emergency Medicine.

[11]  P. Bastard,et al.  Impact of energy storage costs on economical performance in a distribution substation , 2005, IEEE Transactions on Power Systems.

[12]  Firas Obeidat,et al.  A comprehensive review of future photovoltaic systems , 2018 .

[13]  Zhe Chen,et al.  Optimal reactive power dispatch of a full-scale converter based wind farm considering loss minimization , 2019 .

[14]  Thomas Blaschke,et al.  ‘Energy landscapes’: Meeting energy demands and human aspirations , 2013, Biomass & bioenergy.

[15]  Malcolm McCulloch,et al.  Levelized cost of electricity for solar photovoltaic and electrical energy storage , 2017 .

[16]  S. Drouilhet,et al.  A Battery Life Prediction Method for Hybrid Power Applications Preprint , 1997 .

[17]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[18]  S. Holler,et al.  Integration of Solar Thermal Systems in Existing District Heating Systems , 2016 .

[19]  Hailong Li,et al.  A review of the pricing mechanisms for district heating systems , 2015 .

[20]  Tariq Muneer,et al.  Solar Radiation Fundamentals and PV System Components , 2016 .

[21]  Jacquelynne Hernandez,et al.  DOE global energy storage database — A platform for large scale data analytics and system performance metrics , 2016, 2016 IEEE International Conference on Power System Technology (POWERCON).

[22]  D. K. Maly,et al.  Optimal battery energy storage system (BESS) charge scheduling with dynamic programming , 1995 .