A Simulation-Optimization Model for Solar PV Panel Selection Under Solar Irradiance and Load Uncertainty

       [1]        Villarreal, S., et al., Designing a sustainable and distributed generation system for semiconductor wafer fabs. IEEE Transactions on Automation Science and Engineering, 2012. 10(1): p. 16-26.

       [2]        Lopez, P., et al. Effective utilization (Ue)-a breakthrough performance indicator for machine efficiency improvement. in ISSM 2005, IEEE International Symposium on Semiconductor Manufacturing, 2005. 2005. IEEE.

       [3]        Taboada, H., et al. Exploring a solar photovoltaic-based energy solution for green manufacturing industry. in 2012 IEEE International Conference on Automation Science and Engineering (CASE). 2012. IEEE.

       [4]        Moon, J.-Y. and J. Park, Smart production scheduling with time-dependent and machine-dependent electricity cost by considering distributed energy resources and energy storage. International Journal of Production Research, 2014. 52(13): p. 3922-3939.

       [5]        Mousa, O.B., S. Kara, and R.A. Taylor, Comparative energy and greenhouse gas assessment of industrial rooftop-integrated PV and solar thermal collectors. Applied energy, 2019. 241: p. 113-123.

       [6]        Martinez-Rubio, A., F. Sanz-Adan, and J. Santamaria, Optimal design of photovoltaic energy collectors with mutual shading for pre-existing building roofs. Renewable Energy, 2015. 78: p. 666-678.

       [7]        Dehghani, E., et al., Resilient solar photovoltaic supply chain network design under business-as-usual and hazard uncertainties. 2018. 111: p. 288-310.

       [8]        Kharaji Manouchehrabadi, M., S.J.J.o.R. Yaghoubi, and S. Energy, Solar cell supply chain coordination and competition under government intervention. 2019. 11(2): p. 023701.

       [9]        Manouchehrabadi, M.K., S. Yaghoubi, and J.J.R.E. Tajik, Optimal scenarios for solar cell supply chain considering degradation in powerhouses. 2020. 145: p. 1104-1125.

     [10]      Kharaji Manouchehrabadi, M., S.J.E.S. Yaghoubi, Part A: Recovery, Utilization,, and E. Effects, A game theoretic incentive model for closed-loop solar cell supply chain by considering government role. 2020: p. 1-25.

     [11]      Dehghani, E., M.S. Jabalameli, and A.J.E. Jabbarzadeh, Robust design and optimization of solar photovoltaic supply chain in an uncertain environment. 2018. 142: p. 139-156.

     [12]      Wang, B. and X. Xia, Optimal maintenance planning for building energy efficiency retrofitting from optimization and control system perspectives. Energy and Buildings, 2015. 96: p. 299-308.

     [13]      Hosseinalizadeh, R., et al., Economic sizing of a hybrid (PV–WT–FC) renewable energy system (HRES) for stand-alone usages by an optimization-simulation model: case study of Iran. Renewable and Sustainable Energy Reviews, 2016. 54: p. 139-150.

     [14]      Talavera, D., et al., Levelised cost of electricity in high concentrated photovoltaic grid connected systems: spatial analysis of Spain. Applied energy, 2015. 151: p. 49-59.

     [15]      Belmili, H., et al., Sizing stand-alone photovoltaic–wind hybrid system: Techno-economic analysis and optimization. Renewable and Sustainable Energy Reviews, 2014. 30: p. 821-832.

     [16]      Talavera, D., et al., A new approach to sizing the photovoltaic generator in self-consumption systems based on cost–competitiveness, maximizing direct self-consumption. Renewable energy, 2019. 130: p. 1021-1035.

     [17]      Wu, B., et al., Optimal design of stand-alone reverse osmosis desalination driven by a photovoltaic and diesel generator hybrid system. Solar Energy, 2018. 163: p. 91-103.

     [18]      18.       Marnay, C., et al., Optimal technology selection and operation of commercial-building microgrids. IEEE Transactions on Power Systems, 2008. 23(3): p. 975-982.

     [19]      Ferrari, S. and M. Beccali, Energy-environmental and cost assessment of a set of strategies for retrofitting a public building toward nearly zero-energy building target. Sustainable cities and society, 2017. 32: p. 226-234.

     [20]      Fan, Y. and X. Xia, A multi-objective optimization model for energy-efficiency building envelope retrofitting plan with rooftop PV system installation and maintenance. Applied Energy, 2017. 189: p. 327-335.

     [21]      Rysanek, A. and R. Choudhary, Optimum building energy retrofits under technical and economic uncertainty. Energy and Buildings, 2013. 57: p. 324-337.

     [22]      Nottrott, A., J. Kleissl, and B. Washom, Energy dispatch schedule optimization and cost benefit analysis for grid-connected, photovoltaic-battery storage systems. Renewable Energy, 2013. 55: p. 230-240.

     [23]      Carpinelli, G., P. Caramia, and P. Varilone, Multi-linear Monte Carlo simulation method for probabilistic load flow of distribution systems with wind and photovoltaic generation systems. Renewable Energy, 2015. 76: p. 283-295.

     [24]      Wang, C., et al., Robust optimization for load scheduling of a smart home with photovoltaic system. Energy Conversion and Management, 2015. 102: p. 247-257.

     [25]      Akbari, K., et al., Optimal investment and unit sizing of distributed energy systems under uncertainty: A robust optimization approach. Energy and Buildings, 2014. 85: p. 275-286.

     [26]      Murata, A., H. Ohtake, and T. Oozeki, Modeling of uncertainty of solar irradiance forecasts on numerical weather predictions with the estimation of multiple confidence intervals. Renewable energy, 2018. 117: p. 193-201.

     [27]      Abdelsalam, A.A. and E.F. El-Saadany, Probabilistic approach for optimal planning of distributed generators with controlling harmonic distortions. IET Generation, Transmission & Distribution, 2013. 7(10): p. 1105-1115.

     [28]      Tan, B., et al., Optimal selection of energy efficiency measures for energy sustainability of existing buildings. Computers & Operations Research, 2016. 66: p. 258-271.

     [29]      Jafari, A. and V. Valentin, An optimization framework for building energy retrofits decision-making. Building and Environment, 2017. 115: p. 118-129.

     [30]      Wu, Z., B. Wang, and X. Xia, Large-scale building energy efficiency retrofit: Concept, model and control. Energy, 2016. 109: p. 456-465.

     [31]      Liu, Y., et al., Cost-benefit analysis for Energy Efficiency Retrofit of existing buildings: A case study in China. Journal of cleaner production, 2018. 177: p. 493-506.

     [32]      Wang, B., X. Xia, and J. Zhang, A multi-objective optimization model for the life-cycle cost analysis and retrofitting planning of buildings. Energy and Buildings, 2014. 77: p. 227-235.

     [33]      Malatji, E.M., J. Zhang, and X. Xia, A multiple objective optimisation model for building energy efficiency investment decision. Energy and Buildings, 2013. 61: p. 81-87.

     [34]      Valdiserri, P. and C. Biserni, Energy performance of an existing office building in the northern part of Italy: Retrofitting actions and economic assessment. Sustainable cities and society, 2016. 27: p. 65-72.

     [35]      Mulder, G., et al., The dimensioning of PV-battery systems depending on the incentive and selling price conditions. Applied energy, 2013. 111: p. 1126-1135.

     [36]      Ascione, F., et al., Resilience of robust cost-optimal energy retrofit of buildings to global warming: A multi-stage, multi-objective approach. Energy and Buildings, 2017. 153: p. 150-167.

     [37]      Pratama, Y.W., et al., Multi-objective optimization of a multiregional electricity system in an archipelagic state: The role of renewable energy in energy system sustainability. Renewable and Sustainable Energy Reviews, 2017. 77: p. 423-439.

     [38]      Balaban, O. and J.A.P. de Oliveira, Sustainable buildings for healthier cities: assessing the co-benefits of green buildings in Japan. Journal of cleaner production, 2017. 163: p. S68-S78.

     [39]      Radhi, H., On the optimal selection of wall cladding system to reduce direct and indirect CO2 emissions. Energy, 2010. 35(3): p. 1412-1424.

     [40]      Fetanat, A. and E. Khorasaninejad, Size optimization for hybrid photovoltaic–wind energy system using ant colony optimization for continuous domains based integer programming. Applied Soft Computing, 2015. 31: p. 196-209.

     [41]      Shakouri, H., et al., Multi-objective optimization-simulation model to improve the buildings’ design specification in different climate zones of Iran. Sustainable cities and society, 2018. 40: p. 394-415.

     [42]      Pazouki, M., et al., A fuzzy robust multi-objective optimization model for building energy retrofit considering utility function: a university building case study. 2021: p. 110933.

     [43]      Vakili, M., et al., Using artificial neural networks for prediction of global solar radiation in Tehran considering particulate matter air pollution. Energy Procedia, 2015. 74: p. 1205-1212.

     [44]      Sobu, A. and G. Wu. Optimal operation planning method for isolated micro grid considering uncertainties of renewable power generations and load demand. in IEEE PES Innovative Smart Grid Technologies. 2012. IEEE.

     [45]      Laronde, R., A. Charki, and D. Bigaud, Lifetime estimation of a photovoltaic module based on temperature measurement. 2nd IMEKO TC, 2011. 11: p. 15-17.

     [46]      Tazvinga, H., X. Xia, and J. Zhang, Minimum cost solution of photovoltaic–diesel–battery hybrid power systems for remote consumers. Solar Energy, 2013. 96: p. 292-299.

     [47]      Zhu, B., H. Tazvinga, and X. Xia, Switched model predictive control for energy dispatching of a photovoltaic-diesel-battery hybrid power system. IEEE Transactions on Control Systems Technology, 2014. 23(3): p. 1229-1236.

     [48]      Okido, S. and A. Takeda, Economic and environmental analysis of photovoltaic energy systems via robust optimization. Energy Systems, 2013. 4(3): p. 239-266.

     [49]      Mohamed, N.M., S.N.A. Zaine, and R.M. Ramli. Evaluation of CO2 emission from dye solar cell panel production process. in AIP Conference Proceedings. 2016. AIP Publishing.

     [50]      Ming, M., et al., Multi-objective optimization of hybrid renewable energy system using an enhanced multi-objective evolutionary algorithm. Energies, 2017. 10(5): p. 674.

     [51]      Campoccia, A., et al., Comparative analysis of different supporting measures for the production of electrical energy by solar PV and Wind systems: Four representative European cases. Solar Energy, 2009. 83(3): p. 287-297.

     [52]      Couture, T.D., et al., Policymaker's guide to feed-in tariff policy design. 2010, National Renewable Energy Lab.(NREL), Golden, CO (United States).

     [53]      Nuño, E., et al., Simulation of transcontinental wind and solar PV generation time series. Renewable energy, 2018. 118: p. 425-436.