Optimizing the integration of renewable energy sources, energy efficiency, and flexibility solutions in a multi-network pharmaceutical industry

Main Article Content

Francesco Ghionda
https://orcid.org/0000-0001-5338-3226
Alessandro Sartori
https://orcid.org/0009-0007-1237-3946
Zijie Liu
https://orcid.org/0009-0000-0622-4708
Md Shahriar Mahbub
https://orcid.org/0000-0001-8629-3980
Francesco Pilati
https://orcid.org/0000-0002-6085-0985
Matteo Brunelli
https://orcid.org/0000-0002-4291-2150
Diego Viesi
https://orcid.org/0000-0003-0254-9112

Abstract

In the contemporary landscape, roughly one-fourth of worldwide carbon dioxide emissions stem from industrial energy usage. In the industrial sector, improving the efficient and flexible coupling among different energy demands (electricity, heating, and cooling) and exploiting the integration of Renewable Energy Sources (RESs) and waste heat can lead to a drastic reduction in CO2 emissions, these are also the goals of the EU founded Horizon Europe FLEXIndustries project.


This study aims to establish a cost-optimized decarbonization strategy for an energy-intensive industry, focusing on an Italian pharmaceutical company. It delves into the exploration of potential pathways and diverse energy mix configurations. The approach undertaken involves coupling a customized energy system simulation framework, specifically designed for the industrial site, with a Multi-Objective Evolutionary Algorithm (MOEA). The study, conducted with a focus on the year 2024, involves a comparative analysis of three distinct scenarios. Within the intricate and challenging constraints of the industrial demo site, 13 technologies were investigated. The outcomes of each scenario identify a set of 500 Pareto optimal solutions, obtained through 40,000 simulations. These results shed light on the compelling potential of hybrid solutions, showcasing the feasibility of achieving substantial decarbonization with only moderate increases in costs. The availability of land for RES technologies, along with the existence of a biomass supply chain in the region, emerge as pivotal determinants.

Article Details

How to Cite
Ghionda, F., Sartori, A., Liu, Z., Mahbub, M. S., Pilati, F., Brunelli, M., & Viesi, D. (2024). Optimizing the integration of renewable energy sources, energy efficiency, and flexibility solutions in a multi-network pharmaceutical industry. International Journal of Sustainable Energy Planning and Management, 41, 87–107. https://doi.org/10.54337/ijsepm.8167
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Articles

References

Kyoto Protocol to the United Nations Framework Convention on Climate Change. | UNFCCC n.d. https://unfccc.int/documents/2409 (accessed November 28, 2023).

Adoption of the Paris Agreement. Proposal by the President. | UNFCCC n.d. https://unfccc.int/documents/9064 (accessed November 28, 2023).

The European Green Deal. Eur Comm - Eur Comm n.d. https://ec.europa.eu/commission/presscorner/detail/en/ip_19_6691 (accessed November 28, 2023).

U.S. Launches Net-Zero World Initiative to Accelerate Global Energy System Decarbonization | Department of Energy n.d. https://www.energy.gov/articles/us-launches-net-zero-world-initiative-accelerate-global-energy-system-decarbonization (accessed March 19, 2024).

Davis SJ, Lewis NS, Shaner M, Aggarwal S, Arent D, Azevedo IL, et al. Net-zero emissions energy systems. Science 2018;360:eaas9793. https://doi.org/10.1126/science.aas9793.

Renewable energy directive n.d. https://energy.ec.europa.eu/topics/renewable-energy/renewable-energy-directive-targets-and-rules/renewable-energy-directive_en (accessed March 19, 2024).

On the management of wind power intermittency. Renew Sustain Energy Rev 2013;28:643–53. https://doi.org/10.1016/j.rser.2013.08.034.

Javaid N, Hafeez G, Iqbal S, Alrajeh N, Alabed MS, Guizani M. Energy Efficient Integration of Renewable Energy Sources in the Smart Grid for Demand Side Management. IEEE Access 2018;6:77077–96. https://doi.org/10.1109/ACCESS.2018.2866461.

Heffron R, Körner M-F, Wagner J, Weibelzahl M, Fridgen G. Industrial demand-side flexibility: A key element of a just energy transition and industrial development. Appl Energy 2020;269:115026. https://doi.org/10.1016/j.apenergy.2020.115026.

Lund H, Østergaard PA, Connolly D, Mathiesen BV. Smart energy and smart energy systems. Energy 2017;137:556–65. https://doi.org/10.1016/j.energy.2017.05.123.

Geidl M, Koeppel G, Favre-Perrod P, Klockl B, Andersson G, Frohlich K. Energy hubs for the future. IEEE Power Energy Mag 2007;5:24–30. https://doi.org/10.1109/MPAE.2007.264850.

Geidl M, Andersson G. Optimal Coupling of Energy Infrastructures. 2007 IEEE Lausanne Power Tech, Lausanne, Switzerland: IEEE; 2007, p. 1398–403. https://doi.org/10.1109/PCT.2007.4538520.

Lund H, Østergaard PA, Connolly D, Ridjan I, Mathiesen BV, Hvelplund F, et al. Energy Storage and Smart Energy Systems. Int J Sustain Energy Plan Manag 2016;11:3–14. https://doi.org/10.5278/ijsepm.2016.11.2.

Singh VK, Henriques CO, Martins AG. A multiobjective optimization approach to support end-use energy efficiency policy design – the case-study of India. Int J Sustain Energy Plan Manag 2019;23. https://doi.org/10.5278/ijsepm.2408.

Prina MG, Moser D, Vaccaro R, Sparber W. EPLANopt optimization model based on EnergyPLAN applied at regional level: the future competition on excess electricity production from renewables. Int J Sustain Energy Plan Manag 2020;27:35–50. https://doi.org/10.5278/ijsepm.3504.

Al Hasibi RA. Multi-objective Analysis of Sustainable Generation Expansion Planning based on Renewable Energy Potential: A case study of Bali Province of Indonesia. Int J Sustain Energy Plan Manag 2021:189-210 Pages. https://doi.org/10.5278/IJSEPM.6474.

Prina MG, Cozzini M, Garegnani G, Moser D, Oberegger UF, Vaccaro R, et al. Smart energy systems applied at urban level: the case of the municipality of Bressanone-Brixen. Int J Sustain Energy Plan Manag 2016;10:33–52. https://doi.org/10.5278/ijsepm.2016.10.4.

Oree V, Hassen SZS, Fleming PJ. Generation expansion planning optimisation with renewable energy integration: A review. Renew Sustain Energy Rev 2017;69:790–803. https://doi.org/10.1016/j.rser.2016.11.120.

Massȇ P, Gibrat R. Application of Linear Programming to Investments in the Electric Power Industry. Manag Sci 1957;3:149–66. https://doi.org/10.1287/mnsc.3.2.149

Schaeffer PV, Cherene LJ. The inclusion of ‘spinning reserves’ in investment and simulation models for electricity generation. Eur J Oper Res 1989;42:178–89. https://doi.org/10.1016/0377-2217(89)90320-2

Ramos A, Perez-Arriaga IJ, Bogas J. A nonlinear programming approach to optimal static generation expansion planning. IEEE Trans Power Syst 1989;4:1140–6. https://doi.org/10.1109/59.32610.

Cedeño EB, Arora S. Integrated transmission and generation planning model in a deregulated environment. Front Energy 2013;7:182–90. https://doi.org/10.1007/s11708-013-0256-8.

Liu Z, Chakraborty A, He T, Karimi IA. Technoeconomic and environmental optimization of combined heat and power systems with renewable integration for chemical plants. Appl Therm Eng 2023;219:119474. https://doi.org/10.1016/j.applthermaleng.2022.119474.

Morales Sandoval DA, Saikia P, De la Cruz-Loredo I, Zhou Y, Ugalde-Loo CE, Bastida H, et al. A framework for the assessment of optimal and cost-effective energy decarbonisation pathways of a UK-based healthcare facility11The short version of the paper was presented at ICAE2022, Bochum, Germany, Aug 8–11, 2022. This paper is a substantial extension of the short version of the conference paper. Appl Energy 2023;352:121877. https://doi.org/10.1016/j.apenergy.2023.121877.

Gabrielli P, Gazzani M, Martelli E, Mazzotti M. Optimal design of multi-energy systems with seasonal storage. Appl Energy 2018;219:408–24. https://doi.org/10.1016/j.apenergy.2017.07.142.

Mahbub MS, Cozzini M, Østergaard PA, Alberti F. Combining multi-objective evolutionary algorithms and descriptive analytical modelling in energy scenario design. Appl Energy 2016;164:140–51. https://doi.org/10.1016/j.apenergy.2015.11.042.

Mahbub MS, Viesi D, Cattani S, Crema L. An innovative multi-objective optimization approach for long-term energy planning. Appl Energy 2017;208:1487–504. https://doi.org/10.1016/j.apenergy.2017.08.245.

Lund H, Thellufsen JZ, Østergaard PA, Sorknæs P, Skov IR, Mathiesen BV. EnergyPLAN – Advanced analysis of smart energy systems. Smart Energy 2021;1:100007. https://doi.org/10.1016/j.segy.2021.100007.

Viesi D, Crema L, Mahbub MS, Verones S, Brunelli R, Baggio P, et al. Integrated and dynamic energy modelling of a regional system: A cost-optimized approach in the deep decarbonisation of the Province of Trento (Italy). Energy 2020;209:118378. https://doi.org/10.1016/j.energy.2020.118378.

Mahbub MS, Viesi D, Crema L. Designing optimized energy scenarios for an Italian Alpine valley: the case of Giudicarie Esteriori. Energy 2016;116:236–49. https://doi.org/10.1016/j.energy.2016.09.090.

Viesi D, Mahbub MS, Brandi A, Thellufsen JZ, Østergaard PA, Lund H, et al. Multi-objective optimization of an energy community: an integrated and dynamic approach for full decarbonisation in the European Alps. Int J Sustain Energy Plan Manag 2023;38:8–29. https://doi.org/10.54337/ijsepm.7607.

de Maigret J, Viesi D, Mahbub MS, Testi M, Cuonzo M, Thellufsen JZ, et al. A multi-objective optimization approach in defining the decarbonization strategy of a refinery. Smart Energy 2022;6:100076. https://doi.org/10.1016/j.segy.2022.100076.

Delgarm N, Sajadi B, Kowsary F, Delgarm S. Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO). Appl Energy 2016;170:293–303. https://doi.org/10.1016/j.apenergy.2016.02.141.

Xu J, Chen Y, Wang J, Lund PD, Wang D. Ideal scheme selection of an integrated conventional and renewable energy system combining multi-objective optimization and matching performance analysis. Energy Convers Manag 2022;251:114989. https://doi.org/10.1016/j.enconman.2021.114989.

Sharafi M, ELMekkawy TY. Multi-objective optimal design of hybrid renewable energy systems using PSO-simulation based approach. Renew Energy 2014;68:67–79. https://doi.org/10.1016/j.renene.2014.01.011.

Ren F, Wang J, Zhu S, Chen Y. Multi-objective optimization of combined cooling, heating and power system integrated with solar and geothermal energies. Energy Convers Manag 2019;197:111866. https://doi.org/10.1016/j.enconman.2019.111866.

GME - Gestore dei Mercati Energetici SpA 2023. https://www.mercatoelettrico.org/En/Default.aspx (accessed November 28, 2023).

Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 2002;6:182–97. https://doi.org/10.1109/4235.996017.

Mana AA, Kaitouni SI, Kousksou T, Jamil A. Enhancing sustainable energy conversion: Comparative study of superheated and recuperative ORC systems for waste heat recovery and geothermal applications, with focus on 4E performance. Energy 2023;284:128654. https://doi.org/10.1016/j.energy.2023.128654.

Shi L, Shu G, Tian H, Deng S. A review of modified Organic Rankine cycles (ORCs) for internal combustion engine waste heat recovery (ICE-WHR). Renew Sustain Energy Rev 2018;92:95–110. https://doi.org/10.1016/j.rser.2018.04.023.

National Renewable Energy Laboratory. System Advisor Model n.d.

Bolognese M, Viesi D, Bartali R, Crema L. Modeling study for low-carbon industrial processes integrating solar thermal technologies. A case study in the Italian Alps: The Felicetti Pasta Factory. Sol Energy 2020;208:548–58. https://doi.org/10.1016/j.solener.2020.07.091.

Comunità energetiche - Requisiti di accesso n.d. https://www.gse.it/servizi-per-te/autoconsumo/gruppi-di-autoconsumatori-e-comunita-di-energia-rinnovabile/requisiti-di-accesso (accessed November 29, 2023).