Optimization on Photovoltaics and Energy Storage Integrated Flexible Direct Current Distribution Systems of Buildings Considering Load Uncertainty Using Scenario Generation Method
DOI:
https://doi.org/10.13052/dgaej2156-3306.3924Keywords:
Stochastic programming, scenario generation method, independent scenario optimization, energy flexibility, energy storageAbstract
This study explores the intricate challenge of energy demand uncertainty in the design of Photovoltaics and Energy Storage integrated Flexible Direct Current Distribution (PEDF) systems. Our objective is to examine the impact of different scenario generation methods on PEDF system optimization. We compare four approaches, including probabilistic techniques based on Monte Carlo simulation, Latin Hypercube Sampling for base scenario sampling, and a simulation-based method using building performance modeling. We evaluate these approaches using the Independent Scenario Optimization (ISO) and Two-Stage Stochastic Programming (TSSP) models, aiming to minimize the annual total cost within PEDF systems while addressing uncertainties. Our findings shed light on the optimal PEDF design under uncertainty, offering valuable insights for future decision-making in dynamic energy systems.
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References
R. Taleski and D. Rajicic, “Distribution network reconfiguration for energy loss reduction,” IEEE Transactions on Power Systems, vol. 12, no. 1, pp. 398–406, 1997.
X. Liu, X. Liu, Y. Jiang, T. Zhang, and B. Hao, “Photovoltaics and Energy Storage Integrated Flexible Direct Current Distribution Systems of Buildings: Definition, Technology Review, and Application,” CSEE Journal of Power and Energy Systems, vol. 9, no. 3, pp. 829–845, 2023, doi: 10.17775/CSEEJPES.2022.04850.
E. Guevara, F. Babonneau, T. Homem-de-Mello, and S. Moret, “A machine learning and distributionally robust optimization framework for strategic energy planning under uncertainty,” Applied Energy, vol. 271, no. June, 2020, doi: 10.1016/j.apenergy.2020.115005.
R. Nagaraj, D. T. Murthy, and M. M. Rajput, “Modeling Renewables Based Hybrid Power System with Desalination Plant Load Using Neural Networks,” Distributed Generation and Alternative Energy Journal, vol. 34, no. 1, pp. 32–46, 2019, doi: 10.1080/21563306.2019.12054396.
A. Ghouari, C. Hamouda, A. Chaghi, and M. Chahdi, “An Experimental Study on the Smart Home Concept with PV and Energy Management and Control Strategy Using an Open Source Platform,” Distributed Generation and Alternative Energy Journal, vol. 34, no. 1, pp. 61–80, 2019, doi: 10.1080/21563306.2019.12054398.
S. Tindemans and G. Strbac, “Accelerating system adequacy assessment using the multilevel Monte Carlo approach,” Electric Power Systems Research, vol. 189, 2020, doi: 10.1016/j.epsr.2020.106740.
H. Shuai and H. He, “Online Scheduling of a Residential Microgrid via Monte-Carlo Tree Search and a Learned Model,” IEEE Transactions on Smart Grid, vol. 12, no. 2, pp. 1073–1087, Mar. 2021, doi: 10.1109/ TSG.2020.3035127.
A. Ehsan and Q. Yang, “State-of-the-art techniques for modelling of uncertainties in active distribution network planning: A review,” Applied Energy, vol. 239, pp. 1509–1523, 2019, doi: 10.1016/j.apenergy.2019. 01.211.
A. De Paola, D. Papadaskalopoulos, D. Angeli, and G. Strbac, “A game-theoretic modeling approach for merchant transmission planning,” in International Conference on the European Energy Market, EEM, 2018, vol. 2018-June, pp. 1–5. doi: 10.1109/EEM.2018.8469209.
A. Saxena, V. Tirth, and G. Srivastava, “Design and performance analysis of a solar air heater with high heat storage,” Distributed Generation and Alternative Energy Journal, vol. 29, no. 3, pp. 35–55, 2014, doi: 10.1080/21563306.2014.10879016.
M. D. Madavar, M. G. Nezhad, D. A. Aslani, and D. M. Naaraonja, “Analysis of Generations of Wind Power Technologies Based on Technology Life Cycle Approach,” Distributed Generation & Alternative Energy Journal, pp. 52–79, 2017, doi: 10.13052/dgaej2156-3306.3233.
M. Kermani, E. Shirdare, G. Parise, M. Bongiorno, and L. Martirano, “A Comprehensive Technoeconomic Solution for Demand Control in Ports: Energy Storage Systems Integration,” IEEE Transactions on Industry Applications, vol. 58, no. 2, pp. 1592–1601, 2022, doi: 10.1109/TIA.2022.3145769.
C. Charalambous, C. Heracleous, A. Michael, and V. Efthymiou, “Hybrid AC-DC distribution system for building integrated photovoltaics and energy storage solutions for heating-cooling purposes. A case study of a historic building in Cyprus,” Renewable Energy, vol. 216, 2023, doi: 10.1016/j.renene.2023.119032.
E. Heydarian-Forushani, S. Ben Elghali, M. Zerrougui, M. La Scala, and P. Mestre, “A Centralized-Stochastic Solution for Smart Energy Management in a Virtual Power Plant,” pp. 1–5, 2021, doi: 10.1109/eeeic/icpseurope51590.2021.9584773.
A. Sohani et al., “Building integrated photovoltaic/thermal technologies in Middle Eastern and North African countries: Current trends and future perspectives,” Renewable and Sustainable Energy Reviews, vol. 182, 2023, doi: 10.1016/j.rser.2023.113370.
A. Taşer, B. K. Koyunbaba, and T. Kazanasmaz, “Thermal, daylight, and energy potential of building-integrated photovoltaic (BIPV) systems: A comprehensive review of effects and developments,” Solar Energy, vol. 251, pp. 171–196, 2023, doi: 10.1016/j.solener.2022.12.039.
G. Aruta, F. Ascione, N. Bianco, and G. M. Mauro, “Optimization of a diabatic compressed air energy storage coupled with photovoltaics for buildings: CO2-eq emissions vs payback time,” Energy Reports, vol. 8, pp. 12686–12698, 2022, doi: 10.1016/j.egyr.2022.09.112.
C. Şirin, J. Goggins, and M. Hajdukiewicz, “A review on building-integrated photovoltaic/thermal systems for green buildings,” Applied Thermal Engineering, vol. 229, 2023, doi: 10.1016/j.applthermaleng.20 23.120607.
M. Azimian, V. Amir, R. Habibifar, and H. Golmohamadi, “Probabilistic optimization of networked multi-carrier microgrids to enhance resilience leveraging demand response programs,” Sustainability (Switzerland), vol. 13, no. 11, 2021, doi: 10.3390/su13115792.
A. Maturo, C. Vallianos, A. Buonomano, and A. Athienitis, “A novel multi-level predictive management strategy to optimize phase-change energy storage and building-integrated renewable technologies operation under dynamic tariffs,” Energy Conversion and Management, vol. 291, 2023, doi: 10.1016/j.enconman.2023.117220.
W. Chen, X. Li, and X. Pei, “Probabilistic Load Flow Calculation Method Based on Polynomial Normal Transformation and Extended Latin hypercube,” in 2019 3rd IEEE Conference on Energy Internet and Energy System Integration: Ubiquitous Energy Network Connecting Everything, EI2 2019, 2019, pp. 1369–1374. doi: 10.1109/EI247390. 2019.9061908.
R. K. Majji, J. P. Mishra, and A. A. Dongre, “Model predictive control based autonomous DC microgrid integrated with solar photovoltaic system and composite energy storage,” Sustainable Energy Technologies and Assessments, vol. 54, 2022, doi: 10.1016/j.seta.2022.102862.
A. Erdmann et al., “A comparative co-simulation analysis to improve the sustainability of cogeneration-based district multi-energy systems using photovoltaics, power-to-heat, and heat storage,” Energy Conversion and Management, vol. 291, 2023, doi: 10.1016/j.enconman.2023.117226.
