Modeling and optimization of a multi-carrier renewable energy system for zero-energy consumption buildings

Abstract

For the carbon-neutral, a multi-carrier renewable energy system (MRES), driven by the wind, solar and geothermal, was considered as an effective solution to mitigate CO₂ emissions and reduce energy usage in the building sector. A proper sizing method was essential for achieving the desired 100% renewable energy system of resources. This paper presented a bi-objective optimization formulation for sizing the MRES using a constrained genetic algorithm (GA) coupled with the loss of power supply probability (LPSP) method to achieve the minimal cost of the system and the reliability of the system to the load real time requirement. An optimization App has been developed in MATLAB environment to offer a user-friendly interface and output the optimized design parameters when given the load demand. A case study of a swimming pool building was used to demonstrate the process of the proposed design method. Compared to the conventional distributed energy system, the MRES is feasible with a lower annual total cost (ATC). Additionally, the ATC decreases as the power supply reliability of the renewable system decreases. There is a decrease of 24% of the annual total cost when the power supply probability is equal to 8% compared to the baseline case with 0% power supply probability.

Keywords

multi-carrier renewable energy system / constrained genetic algorithm / loss of power supply probability (LPSP) method / zero-energy consumption building / optimal device capacity

References

[1] WIN T, MAKTIN H. Shell world energy model: A view to 2100 [R]. Shell International BV, 2017.

[2] BenatiallahA, BenatiallahD, GhaitaouiT, et al.. Modelling and simulation of renewable energy systems in Algeria [J]. International Journal of Science and Applied Information Technology, 2017, 7(1): 17-22

[3] GhianiE, PisanoG. Impact of renewable energy sources and energy storage technologies on the operation and planning of smart distribution networks [M]. Operation of Distributed Energy Resources in Smart Distribution Networks, 2018, Amsterdam, Elsevier, 2548

[4] EvansA, StrezovV, EvansT J. Assessment of sustainability indicators for renewable energy technologies [J]. Renewable and Sustainable Energy Reviews, 2009, 13(5): 1082-1088

[5] BarbosaL S, BogdanovD, VainikkaP, et al.. Hydro, wind and solar power as a base for a 100% renewable energy supply for South and Central America [J]. PLoS One, 2017, 12(3): e0173820

[6] AgbossouK, KolheM L, HamelinJ, et al.. Electrolytic hydrogen based renewable energy system with oxygen recovery and re-utilization [J]. Renewable Energy, 2004, 2981305-1318

[7] IshaqH, DincerI. An efficient energy utilization of biomass energy-based system for renewable hydrogen production and storage [J]. Journal of Energy Resources Technology, 2022, 144(1): 011701

[8] LuoZ, YangS, XieN, et al.. Multi-objective capacity optimization of a distributed energy system considering economy, environment and energy [J]. Energy Conversion and Management, 2019, 200: 112081

[9] ChenY, LinB. Slow diffusion of renewable energy technologies in China: An empirical analysis from the perspective of innovation system [J]. Journal of Cleaner Production, 2020, 261121186

[10] Renewable energy roadmap for China in 2030 [R]. Beijing, China: Energy Research Institute National Development and Reform Commission, 2011. (in Chinese)

[11] LackmannT, HewsonJ C, KnausR C, et al.. Stochastic modeling of unsteady extinction in turbulent non-premixed combustion [J]. Proceedings of the Combustion Institute, 2017, 3621677-1684

[12] AscioneF, BorrelliM, de MasiR F, et al.. Hourly operational assessment of HVAC systems in mediterranean nearly zero-energy buildings: Experimental evaluation of the potential of ground cooling of ventilation air [J]. Renewable Energy, 2020, 155950-968

[13] FengW, ZhangQ, JiH, et al.. A review of net zero energy buildings in hot and humid climates: Experience learned from 34 case study buildings [J]. Renewable and Sustainable Energy Reviews, 2019, 114: 109303

[14] CsoknyaiT, LegardeurJ, AkleA A, et al.. Analysis of energy consumption profiles in residential buildings and impact assessment of a serious game on occupants’ behavior [J]. Energy and Buildings, 2019, 1961-20

[15] SunZ, ZhaoY, XuW, et al.. A solar heating and cooling system in a nearly zero-energy building: A case study in China [J]. International Journal of Photoenergy, 2017, 2017: 2053146

[16] WuW, SkyeH M. Net-zero nation: HVAC and PV systems for residential net-zero energy buildings across the United States [J]. Energy Conversion and Management, 2018, 177605-628

[17] GeidlMIntegrated modeling and optimization of multi-carrier energy systems [D], 2007, Switzerland, Swiss Federal Institute of Technology in Zurich, 13

[18] KIENZLE F, ANDERSSON G. A greenfield approach to the future supply of multiple energy carriers [C]//2009 IEEE Power & Energy Society General Meeting. Calgary, AB, Canada. IEEE: 1–8. DOI: https://doi.org/10.1109/PES.2009.5275692.1-8.

[19] BigliaA, CareddaF V, FabrizioE, et al.. Technical-economic feasibility of CHP systems in large hospitals through the energy Hub method: The case of Cagliari AOB [J]. Energy and Buildings, 2017, 147: 101-112

[20] MancarellaP. MES (multi-energy systems): An overview of concepts and evaluation models [J]. Energy, 2014, 65: 1-17

[21] Mohammadi-IvatlooB, JabariFOperation, planning, and analysis of energy storage systems in smart energy hubs [M], 2018, Cham, Springer International Publishing

[22] MohammadiM, NoorollahiY, Mohammadi-IvatlooB, et al.. Energy hub: From a model to a concept—A review [J]. Renewable and Sustainable Energy Reviews, 2017, 80: 1512-1527

[23] YangH, ZhouW, LuL, et al.. Optimal sizing method for stand-alone hybrid solar-wind system with LPSP technology by using genetic algorithm [J]. Solar Energy, 2008, 82(4): 354-367

[24] ErdincO, UzunogluM. Optimum design of hybrid renewable energy systems: Overview of different approaches [J]. Renewable and Sustainable Energy Reviews, 2012, 16(3): 1412-1425

[25] KoutroulisE, KolokotsaD, PotirakisA, et al.. Methodology for optimal sizing of stand-alone photovoltaic/wind-generator systems using genetic algorithms [J]. Solar Energy, 2006, 80(9): 1072-1088

[26] YangH, ZhouW, LouC. Optimal design and techno-economic analysis of a hybrid solar-wind power generation system [J]. Applied Energy, 2009, 86(2): 163-169

[27] Dufo-LópezR, Bernal-AgustínJ L. Multi-objective design of PV-wind-diesel-hydrogen-battery systems [J]. Renewable Energy, 2008, 33(12): 2559-2572

[28] MohammadiM, HosseinianS H, GharehpetianG B. Optimization of hybrid solar energy sources/wind turbine systems integrated to utility grids as microgrid (MG) under pool/bilateral/hybrid electricity market using PSO [J]. Solar Energy, 2012, 86(1): 112-125

[29] KellyG E, ParkenW HJr.Method of testing, rating and estimating the seasonal performance of central air conditioners and heat pumps operating in the cooling mode [R], 1978, Washington, DC, Center for Building Technology

[30] SongH, DotzauerE, ThorinE, et al.. Annual performance analysis and comparison of pellet production integrated with an existing combined heat and power plant [J]. Bioresource Technology, 2011, 102(10): 6317-6325

[31] FabrizioE, FilippiM, VirgoneJ. An hourly modelling framework for the assessment of energy sources exploitation and energy converters selection and sizing in buildings [J]. Energy and Buildings, 2009, 41(10): 1037-1050

[32] BiglarianH, AbbaspourM, SaidiM H. Evaluation of a transient borehole heat exchanger model in dynamic simulation of a ground source heat pump system [J]. Energy, 2018, 147: 81-93

[33] Vaez-ZadehS, IsfahaniA HMultiobjective optimization of air-core linear permanent magnet synchronous motors for improved thrust and low magnet consumption [C], 2005, 1: 226-229

[34] UmbarkarA J, ShethP D. Crossover operators in genetic algorithms: A review [J]. ICTACT Journal on Soft Computing, 2015, 6(1): 1083-1092

[35] SainiN. Review of selection methods in genetic algorithms [J]. International Journal of Engineering and Computer Science, 2017, 6: 23261-23263

[36] SoniN, KumarT. Study of Various mutation operators in genetic algorithms [J]. International Journal of Computer Science and Information Technologies (IJCSIT), 2014, 5: 4519-4521

[37] MaT, WuJ, HaoL, et al.. The optimal structure planning and energy management strategies of smart multi energy systems [J]. Energy, 2018, 160: 122-141

[38] WuQ, ZhouJ, LiuS, et al.. Multi-objective optimization of integrated renewable energy system considering economics and CO₂ emissions [J]. Energy Procedia, 2016, 104: 15-20

[39] EvinsR. Multi-level optimization of building design, energy system sizing and operation [J]. Energy, 2015, 90: 1775-1789

[40] ZhouZ, LiuP, LiZ, et al.. An engineering approach to the optimal design of distributed energy systems in China [J]. Applied Thermal Engineering, 2013, 53(2): 387-396

[41] AkhavanS S, AhmadiR. Dynamic optimization of solar-wind hybrid system connected to electrical battery or hydrogen as an energy storage system [J]. International Journal of Energy Research, 2021, 45(7): 10630-10654

[42] ZhangJ, ZhouN, HingeA, et al.. Governance strategies to achieve zero-energy buildings in China [J]. Building Research & Information, 2016, 44(5–6): 604-618

[43] SatolaD, KristiansenA B, Houlihan-WibergA, et al.. Comparative life cycle assessment of various energy efficiency designs of a container-based housing unit in China: A case study [J]. Building and Environment, 2020, 186107358

[44] DohertyB, TrenbathK. Device-level plug load disaggregation in a zero energy office building and opportunities for energy savings [J]. Energy and Buildings, 2019, 204109480