Quantitative and qualitative risk-informed energy investment for industrial companies

Abdar, 2020, Energy choices in Alaska : Mining people ’ s perception and attitudes from geotagged tweets, Renew. Sustain. Energy Rev., 124, 10.1016/j.rser.2020.109781 Afman, 2017, Energy and electricity price scenarios Afzali, 2020, Urban community energy systems design under uncertainty for specified levels of carbon dioxide emissions, Appl. Energy, 259, 10.1016/j.apenergy.2019.114084 Alaali, 2020, The effect of oil and stock price volatility on firm level investment: The case of UK firms, Energy Econ., 87, 10.1016/j.eneco.2020.104731 Bhardwaj, 2019, More priorities, more problems? Decision-making with multiple energy, development and climate objectives, Energy Res. Soc. Sci., 49, 143, 10.1016/j.erss.2018.11.003 Bishnoi, 2022, Optimised site selection of hybrid renewable installations for flare gas reduction using Multi-Criteria decision making, Energy Convers. Manag. X, 13 Bohlayer, 2021, Multi-period investment pathways - Modeling approaches to design distributed energy systems under uncertainty, Appl. Energy Borgonovo, 2016, Sensitivity analysis: A review of recent advances, Eur. J. Oper. Res., 248, 869, 10.1016/j.ejor.2015.06.032 Boudreau, 2019, Risk-informed decision-making in asset management as a complex adaptive system of systems, Int. J. Strateg. Eng. Asset. Manag., 3, 198, 10.1504/IJSEAM.2019.108468 Brocal, 2019, Emerging risk management in industry 4.0: An approach to improve organizational and human performance in the complex systems, Complexity, 2019, 10.1155/2019/2089763 Carnovale, 2020, A modeling and experimental study of capacity fade for lithium-ion batteries, Energy AI, 2, 10.1016/j.egyai.2020.100032 Chen, 2016, Regional planning of new-energy systems within multi-period and multi-option contexts: A case study of Fengtai, Beijing, China, Renew. Sustain. Energy Rev., 65, 356, 10.1016/j.rser.2016.07.017 Comission, 2016, Report from the commission to the European Parliament, the Council Coppitters, 2020, Robust design optimization and stochastic performance analysis of a grid-connected photovoltaic system with battery storage and hydrogen storage, Energy, 213, 10.1016/j.energy.2020.118798 Cornejo-Cañamares, 2021, Environmental objectives and non-technological innovation in Spanish manufacturing SMEs, J. Clean. Prod. Cotta, 2021 Das, 2021, Techno-economic optimisation of stand-alone hybrid renewable energy systems for concurrently meeting electric and heating demand, Sustain. Cities Soc., 68, 10.1016/j.scs.2021.102763 Eriksson, 2017, Optimization and integration of hybrid renewable energy hydrogen fuel cell energy systems – A critical review, Appl. Energy, 202, 348, 10.1016/j.apenergy.2017.03.132 Fawcett, 2020, Why & how energy efficiency policy should address SMEs, Energy Policy, 140, 10.1016/j.enpol.2020.111337 Gao, 2022, Research on the energy storage configuration strategy of new energy units, Energy Rep., 8, 659, 10.1016/j.egyr.2022.03.091 Guo, 2022, Cost–benefit analysis of photovoltaic-storage investment in integrated energy systems, Energy Rep., 8, 66, 10.1016/j.egyr.2022.02.158 Gveroski, 2017, Determinants of investment decisions in smes, Balk. Near East. J. Soc. Sci., 03, 71 Harter, 2020, Uncertainty analysis of life cycle energy assessment in early stages of design, Energy Build, 208, 10.1016/j.enbuild.2019.109635 Hoogmartens, 2014, Bridging the gap between LCA, LCC and CBA as sustainability assessment tools, Environ. Impact Assess. Rev., 48, 27, 10.1016/j.eiar.2014.05.001 IEA, 2020. Tracking Industry 2020. Paris. Jordan, 2015, Performance and aging of a 20-year-old silicon PV system, IEEE J. Photovolt., 5, 744, 10.1109/JPHOTOV.2015.2396360 Kakran, 2018, Smart operations of smart grids integrated with distributed generation: A review, Renew. Sustain. Energy Rev., 81, 524, 10.1016/j.rser.2017.07.045 Kaya, 2019, A comprehensive review of fuzzy multi criteria decision making methodologies for energy policy making, Energy Strateg. Rev., 24, 207, 10.1016/j.esr.2019.03.003 Khan, 2021, Optimization and sizing of SPV/Wind hybrid renewable energy system: A techno-economic and social perspective, Energy, 233, 10.1016/j.energy.2021.121114 Kristensen, 2016, Choosing the appropriate sensitivity analysis method for building energy model-based investigations, Energy Build, 130, 166, 10.1016/j.enbuild.2016.08.038 Lekvan, 2021, Robust optimization of renewable-based multi-energy micro-grid integrated with flexible energy conversion and storage devices, Sustain. Cities Soc., 64, 10.1016/j.scs.2020.102532 Lewis, 2000, Direct search methods : Then and now operated by universities space research association, Science, 124, 191 Li, 2022, Coordinated multi-objective capacity optimization of wind-photovoltaic-pumped storage hybrid system, Energy Rep., 8, 1303, 10.1016/j.egyr.2022.08.160 Li, 2020, Probability-interval-based optimal planning of integrated energy system with uncertain wind power, IEEE Trans. Ind. Appl., 56, 4, 10.1109/TIA.2019.2942260 Liberti, 2008, Introduction to global optimization. LIX, École polytech, Adv. Math. Optim. Liu, 2022, A novel distributed energy system combining hybrid energy storage and a multi-objective optimization method for nearly zero-energy communities and buildings, Energy Mansouri, 2020, Two-stage stochastic framework for energy hubs planning considering demand response programs, Energy Mavromatidis, 2018, Uncertainty and global sensitivity analysis for the optimal design of distributed energy systems, Appl. Energy, 214, 219, 10.1016/j.apenergy.2018.01.062 Mavromatidis, 2021, MANGO: A novel optimization model for the long-term, multi-stage planning of decentralized multi-energy systems, Appl. Energy, 288, 10.1016/j.apenergy.2021.116585 MIBGAS, 2021 Mu, 2022, CVaR-based operation optimization method of community integrated energy system considering the uncertainty of integrated demand response, Energy Rep., 8, 1216, 10.1016/j.egyr.2021.11.133 Nieto-Morote, 2011, A fuzzy approach to construction project risk assessment, Int. J. Proj. Manag., 29, 220, 10.1016/j.ijproman.2010.02.002 Noyan, 2012, Risk-averse two-stage stochastic programming with an application to disaster management, Comput. Oper. Res., 39, 541, 10.1016/j.cor.2011.03.017 Omie, 2021 Özbuğday, 2020, Resource efficiency investments and firm performance: Evidence from European SMEs, J. Clean. Prod. Pecenak, 2019, Efficient multi-year economic energy planning in microgrids, Appl. Energy, 255, 10.1016/j.apenergy.2019.113771 Peng, 2019, Investment risk evaluation for new energy resources: An integrated decision support model based on regret theory and ELECTRE III, Energy Convers. Manag., 183, 332, 10.1016/j.enconman.2019.01.015 Petkov, 2022, MANGOret: An optimization framework for the long-term investment planning of building multi-energy system and envelope retrofits, Appl. Energy, 314, 10.1016/j.apenergy.2022.118901 Pickering, 2019, District energy system optimisation under uncertain demand: Handling data-driven stochastic profiles, Appl. Energy, 236, 1138, 10.1016/j.apenergy.2018.12.037 Pye, 2018, Assessing qualitative and quantitative dimensions of uncertainty in energy modelling for policy support in the United Kingdom, Energy Res. Soc. Sci., 46, 332, 10.1016/j.erss.2018.07.028 Qiao, 2022, Research on wind power project investment risk evaluation based on fuzzy-gray clustering trigonometric function, Energy Rep., 8, 1191, 10.1016/j.egyr.2022.02.222 Roszkowska, 2013, Rank ordering criteria weighting methods – a comparative overview, 14 Saaty, 1987, The analytic hierarchy process-what it is and how it is used, Math. Model., 9, 161, 10.1016/0270-0255(87)90473-8 Serrano-Guerrero, 2021, Knowledge-Based Systems Fuzzy logic applied to opinion mining : A review, Knowl.-Based Syst., 222, 10.1016/j.knosys.2021.107018 Solangi, 2019, Evaluating the strategies for sustainable energy planning in Pakistan: An integrated SWOT-AHP and Fuzzy-TOPSIS approach, J. Clean. Prod., 236, 10.1016/j.jclepro.2019.117655 Spanish Government, 2020 Srivastava, 2019 Tian, 2021, Placement and sizing of EESS bundled with uncertainty modeling by two-stage stochastic search based on improved shark smell optimization algorithm in micro-grids, Energy Rep., 7, 4792, 10.1016/j.egyr.2021.07.019 Tran, 2018, Incorporating performance-based global sensitivity and uncertainty analysis into LCOE calculations for emerging renewable energy technologies, Appl. Energy, 216, 157, 10.1016/j.apenergy.2018.02.024 Urbano, 2021, Risk assessment of energy investment in the industrial framework – uncertainty and sensitivity analysis for energy design and operation optimisation, Energy, 239 Urbano, 2021, Energy equipment sizing and operation optimisation for prosumer industrial SMEs – A lifetime approach, Appl. Energy, 299, 10.1016/j.apenergy.2021.117329 Vahedipour-Dahraie, 2020, Stochastic risk-constrained scheduling of renewable-powered autonomous microgrids with demand response actions: Reliability and economic implications, IEEE Trans. Ind. Appl., 56, 1882, 10.1109/TIA.2019.2959549 Wang, 2022, Two-stage robust optimization of thermal-ESS units scheduling under wind uncertainty, Energy Rep., 8, 1147, 10.1016/j.egyr.2022.02.277 Xie, 2021, Optimal sizing of battery energy storage system in smart microgrid considering virtual energy storage system and high photovoltaic penetration, J. Clean. Prod., 281, 10.1016/j.jclepro.2020.125308 Zhang, 2022, Optimal investment portfolio strategies for power enterprises under multi-policy scenarios of renewable energy, Renew. Sustain. Energy Rev., 154, 10.1016/j.rser.2021.111879 Zhang, 2022, Economic evaluation of Wind–PV–Pumped storage hybrid system considering carbon emissions, Energy Rep., 8, 1249, 10.1016/j.egyr.2022.03.001 Zhou, 2021 Zhou, 2019, Long-term forecasts for energy commodities price: What the experts think, Energy Econ., 84, 10.1016/j.eneco.2019.104484