1 [1] Davis, S. J. et al. Net-zero emissions energy systems. Science 360, 9793 (2018).
2 [2] Olauson, J. et al. Net load variability in the Nordic countries with a highly or fully renewable power system. Nat. Energy 1, 1–14 (2016).
3 [3] Sternberg, A. et al. Power-to-what?—Environmental assessment of energy storage systems. Energy Environ. Sci. 8, 389–400 (2015).
4 [4] Comello, S. & Reichelstein, S. The emergence of cost effective battery storage.Nat. Commun. 10, 2038 (2019).
5 [5] Shaner, M. R., Atwater, H. A., Lewis, N. S. & McFarland, E. W. A comparative technoeconomic analysis of renewable hydrogen production using solar energy. Energy Environ. Sci. 9, 2354–2371 (2016).
6 [6] Van Vuuren, D. P. et al. Alternative pathways to the 1.5 ∘C target reduce the need for negative emission technologies. Nat. Clim. Change 8, 391–397 (2018).
7 [7] Parkinson, B., Balcombe, P., Speirs, J. F., Hawkes, A. D. & Hellgardt, K. Levelized cost of CO 2 mitigation from hydrogen production routes. Energy Environ. Sci. 12, 19–40 (2019).
8 [8] Arbabzadeh, M., Sioshansi, R., Johnson, J. X. & Keoleian, G. A. The role of energy storage in deep decarbonization of electricity production. Nat. Commun. 10, 3413 (2019).
9 [9] Sepulveda, N. A., Jenkins, J. D., Edington, A., Mallapragada, D. S. & Lester, R. K. The design space for long-duration energy storage in decarbonized power systems. Nat. Energy 6, 506–516 (2021).
10 [10] Hauch, A. et al. Recent advances in solid oxide cell technology for electrolysis.Science 370, 6513 (2020).
11 [11] van Renssen, S. The hydrogen solution? Nat. Clim. Change 10, 799–801 (2020).
12 [12] Ueckerdt, F. et al. Potential and risks of hydrogen-based e-fuels in climate change mitigation. Nat. Clim. Change 11, 384–393 (2021).
13 [13] Wozabal, D., Graf, C. & Hirschmann, D. The effect of intermittent renewables on the electricity price variance. OR Spectr. 38, 687–709 (2016).
14 [14] Ketterer, J. C. The impact of wind power generation on the electricity price in Germany. Energy Econ. 44, 270–280 (2014).
15 [15] Guerra, O. J. et al. The value of seasonal energy storage technologies for the integration of wind and solar power. Energy Environ. Sci. 13, 1909–1922 (2020).
16 [16] Staffell, I. et al. The role of hydrogen and fuel cells in the global energy system.Energy Environ. Sci. 12, 463–491 (2019).
17 [17] Guerra, O. J., Eichman, J., Kurtz, J. & Hodge, B. M. Cost competitiveness of electrolytic hydrogen. Joule 3, 2425–2443 (2019).
18 [18] Rau, G. H., Willauer, H. D. & Ren, Z. J. The global potential for converting renewable electricity to negative-CO 2-emissions hydrogen. Nat. Clim. Change8, 621–626 (2018).
19 [19] Braff, W. A., Mueller, J. M. & Trancik, J. E. Value of storage technologies for wind and solar energy. Nat. Clim. Change 6, 964–969 (2016).
20 [20] International Energy Agency. The Future of Hydrogen. Tech. Rep. (International Energy Agency, 2019).
21 [21] Ding, H. et al. Self-sustainable protonic ceramic electrochemical cells using a triple conducting electrode for hydrogen and power production. Nat. Commun. 11, 1907 (2020).
22 [22] Regmi, Y. N. et al. A low temperature unitized regenerative fuel cell realizing 60% round trip efficiency and 10,000 cycles of durability for energy storage applications. Energy Environ. Sci. 13, 2096–2105 (2020).
23 [23] Elcogen. Reversible solid oxide cell technology as a power storing solution for renewable energy (Italy). http:
24 [24] Schmidt, O., Melchior, S., Hawkes, A. & Staffell, I. Projecting the future levelized cost of electricity storage technologies. Joule 13, 1–20 (2019).
25 [25] Proost, J. State-of-the art CAPEX data for water electrolysers, and their impact on renewable hydrogen price settings. Int. J. Hydrog. Energy 44, 4406–4413 (2019).
26 [26] Glenk, G., Reichelstein, S. Reversible Power-to-Gas systems for energy conversion and storage. Nat Commun 13, 2010 (2022).
27 [1] A. Brem, et al., Industrial smart and micro grid systems–a systematic mapping study, J. Clean. Prod. 244 (2020) 118828.
28 [2] A.J. Aghbolaghi, et al., Microgrid planning and modeling, Microgrid Architectures, Control and Protection Methods, Springer, 2020, pp. 21–46.
29 [3] H. Hajebrahimi, et al., A new energy management control method for energy sto- rage systems in microgrids, IEEE Trans. Power Electron. (2020).
30 [4] AroaR.Mainar,etal.,Anoverviewofprogressinelectrolytesforsecondaryzinc-air batteries and other storage systems based on zinc, J. Energy Storage 15 (2018) 304–328.
31 [5] M.S. Nazir, et al., Impacts of renewable energy atlas: reaping the benefits of re- newables and biodiversity threats, Int. J. Hydrogen Energy (2020).
32 [6] M.S. Nazir, et al., Improving the performance of doubly fed induction generator using fault tolerant control—a hierarchical approach, Appl. Sci. 10 (3) (2020) 924.
33 [7] M.S.Nazir,W.Qi,Impactofsymmetricalshort-circuitfaultondoubly-fedinduction generator controller, Int. J. Electron. (2020).
34 [8] M. Sedighi, M. Moradzadeh, Impact of demand response program on hybrid renewable energy system planning, Demand Response Application in Smart Grids, Springer, 2020, pp. 215–230.
35 [9] R. Jadeja, et al., Power quality issues and mitigation techniques in microgrid, Microgrid Architectures, Control and Protection Methods, Springer, 2020, pp. 719–748.
36 [10] M. Bajaj, A.K. Singh, Grid integrated renewable DG systems: a review of power quality challenges and state‐of‐the‐art mitigation techniques, Int. J. Energy Res. 44 (1) (2020) 26–69.
37 [11] W. Ahmed, et al., Power quality improving based harmonical studies of a single phase step down bridge-cycloconverter, J. Electr. Syst. 15 (1) (2019) 109–122.
38 [12] A. Gallo, et al., Energy storage in the energy transition context: a technology re- view, Renew. Sustain. Energy Rev. 65 (2016) 800–822.
39 [13] M.D. Al-Falahi, S. Jayasinghe, H. Enshaei, A review on recent size optimization methodologies for standalone solar and wind hybrid renewable energy system, Energy Convers. Manag. 143 (2017) 252–274.
40 [14] M.S. Nazir, et al., Wind generation forecasting methods and proliferation of artifi- cial neural network: a review of five years research trend, Sustainability 12 (9) (2020) 3778.
41 [15] I. Dincer, C. Acar, A review on clean energy solutions for better sustainability, Int. J. Energy Res. 39 (5) (2015) 585–606.
42 [1] K. Hussain, M. N. Mohd Salleh, S. Cheng, Y. Shi, Metaheuristic research: a comprehensive survey, Artif. Intelligence Rev., 52 (4), 2191–2233, 10.1007/ s10462-017-9605-z.
43 [2] S.S.R. M, R. Mallipeddi, K.N. Das, A twin-archive guided decomposition based multi/many-objective evolutionary algorithm, Swarm Evolut. Comp. 71 (2022) 101082, https:
44 [3] I. Boussaïd, J. Lepagnot, P. Siarry, A survey on optimization metaheuristics, Inf. Sci. 237 (2013) 82–117, https:
45 [4] T. Dokeroglu, E. Sevinc, T. Kucukyilmaz, A. Cosar, A survey on new generation metaheuristic algorithms, Comp. Ind. Eng. 137 (2019), https:
46 [5] M.A. Jan, N. Tairan, R. Khanum, W. Mashwani, A new threshold based penalty function embedded MOEA/D, Int. J. Adv. Comp. Sci. Appl. 7 (2016), https:
47 [6] K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evolut. Comput. 6 (2) (2002) 182– 197, https:
48 [7] T.P. Runarsson, Y. Xin, Stochastic ranking for constrained evolutionary optimization, IEEE Trans. Evolut. Comput. 4 (3) (2000) 284–294, https:
49 [8] Z. Fan, W. Li, X. Cai, H. Li, C. Wei, Q. Zhang, K. Deb, E. Goodman, Push and pull search for solving constrained multi-objective optimization problems, Swarm Evolut. Comput. 44 (2019) 665–679.
50 [9] M. Ming, A. Trivedi, R. Wang, D. Srinivasan, T. Zhang, A dual-population-based evolutionary algorithm for constrained multiobjective optimization, IEEE Trans. Evolut. Comput. 25 (4) (2021) 739–753, https:
51 [10] I. Das, J.E. Dennis, Normal-boundary intersection: a new method for generating the Pareto surface in nonlinear multicriteria optimization problems, SIAM J. Optimiz. 8 (3) (1998) 631–657.
52 [11] Y.G. Woldesenbet, G.G. Yen, B.G. Tessema, Constraint handling in multiobjective evolutionary optimization, IEEE Trans. Evolut. Comput. 13 (3) (2009) 514–525, https:
53 [12] M. A. Jan, Q. Zhang, MOEA/D for constrained multiobjective optimization: Some preliminary experimental results, in 2010 UK Workshop on Computational Intelligence (UKCI), 2010, pp. 1-6, doi: https:
54 [13] Z. Ma, Y. Wang, W. Song, A new fitness function with two rankings for evolutionary constrained multiobjective optimization, IEEE Trans. Syst., Man, Cybernetics: Syst. 51 (8) (2021) 5005–5016, https:
55 [14] D. Saxena, T. Ray, K. Deb, A. Tiwari, Constrained many-objective optimization: a way forward. 2009, pp. 545-552, doi: https:
56 [15] T. Takahama, S. Sakai, Constrained optimization by the e constrained differential evolution with gradient-based mutation and feasible elites, in 2006 IEEE International Conference on Evolutionary Computation, 2006, pp. 1–8, doi: https:
57 [16] W. Ying, W. He, Y. Huang, D. Li, Y. Wu, An adaptive stochastic ranking mechanism in MOEA/D for constrained multi-objective optimization, in 2016 International Conference on Information System and Artificial Intelligence (ISAI), 2016, pp. 514–518, doi: https:
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62 [21] Y. Tian, Y. Zhang, Y. Su, X. Zhang, K.C. Tan, Y. Jin, Balancing objective optimization and constraint satisfaction in constrained evolutionary multiobjective optimization, IEEE Trans. Cybernetics 52 (9) (2022) 9559–9572.
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[1] Davis, S. J. et al. Net-zero emissions energy systems. Science 360, 9793 (2018).
[2] Olauson, J. et al. Net load variability in the Nordic countries with a highly or fully renewable power system. Nat. Energy 1, 1–14 (2016).
[3] Sternberg, A. et al. Power-to-what?—Environmental assessment of energy storage systems. Energy Environ. Sci. 8, 389–400 (2015).
[4] Comello, S. & Reichelstein, S. The emergence of cost effective battery storage.Nat. Commun. 10, 2038 (2019).
[5] Shaner, M. R., Atwater, H. A., Lewis, N. S. & McFarland, E. W. A comparative technoeconomic analysis of renewable hydrogen production using solar energy. Energy Environ. Sci. 9, 2354–2371 (2016).
[6] Van Vuuren, D. P. et al. Alternative pathways to the 1.5 ∘C target reduce the need for negative emission technologies. Nat. Clim. Change 8, 391–397 (2018).
[7] Parkinson, B., Balcombe, P., Speirs, J. F., Hawkes, A. D. & Hellgardt, K. Levelized cost of CO 2 mitigation from hydrogen production routes. Energy Environ. Sci. 12, 19–40 (2019).
[8] Arbabzadeh, M., Sioshansi, R., Johnson, J. X. & Keoleian, G. A. The role of energy storage in deep decarbonization of electricity production. Nat. Commun. 10, 3413 (2019).
[9] Sepulveda, N. A., Jenkins, J. D., Edington, A., Mallapragada, D. S. & Lester, R. K. The design space for long-duration energy storage in decarbonized power systems. Nat. Energy 6, 506–516 (2021).
[10] Hauch, A. et al. Recent advances in solid oxide cell technology for electrolysis.Science 370, 6513 (2020).
[11] van Renssen, S. The hydrogen solution? Nat. Clim. Change 10, 799–801 (2020).
[12] Ueckerdt, F. et al. Potential and risks of hydrogen-based e-fuels in climate change mitigation. Nat. Clim. Change 11, 384–393 (2021).
[13] Wozabal, D., Graf, C. & Hirschmann, D. The effect of intermittent renewables on the electricity price variance. OR Spectr. 38, 687–709 (2016).
[14] Ketterer, J. C. The impact of wind power generation on the electricity price in Germany. Energy Econ. 44, 270–280 (2014).
[15] Guerra, O. J. et al. The value of seasonal energy storage technologies for the integration of wind and solar power. Energy Environ. Sci. 13, 1909–1922 (2020).
[16] Staffell, I. et al. The role of hydrogen and fuel cells in the global energy system.Energy Environ. Sci. 12, 463–491 (2019).
[17] Guerra, O. J., Eichman, J., Kurtz, J. & Hodge, B. M. Cost competitiveness of electrolytic hydrogen. Joule 3, 2425–2443 (2019).
[18] Rau, G. H., Willauer, H. D. & Ren, Z. J. The global potential for converting renewable electricity to negative-CO 2-emissions hydrogen. Nat. Clim. Change8, 621–626 (2018).
[19] Braff, W. A., Mueller, J. M. & Trancik, J. E. Value of storage technologies for wind and solar energy. Nat. Clim. Change 6, 964–969 (2016).
[20] International Energy Agency. The Future of Hydrogen. Tech. Rep. (International Energy Agency, 2019).
[21] Ding, H. et al. Self-sustainable protonic ceramic electrochemical cells using a triple conducting electrode for hydrogen and power production. Nat. Commun. 11, 1907 (2020).
[22] Regmi, Y. N. et al. A low temperature unitized regenerative fuel cell realizing 60% round trip efficiency and 10,000 cycles of durability for energy storage applications. Energy Environ. Sci. 13, 2096–2105 (2020).
[23] Elcogen. Reversible solid oxide cell technology as a power storing solution for renewable energy (Italy). http:
[24] Schmidt, O., Melchior, S., Hawkes, A. & Staffell, I. Projecting the future levelized cost of electricity storage technologies. Joule 13, 1–20 (2019).
[25] Proost, J. State-of-the art CAPEX data for water electrolysers, and their impact on renewable hydrogen price settings. Int. J. Hydrog. Energy 44, 4406–4413 (2019).
[26] Glenk, G., Reichelstein, S. Reversible Power-to-Gas systems for energy conversion and storage. Nat Commun 13, 2010 (2022).
[27] A. Brem, et al., Industrial smart and micro grid systems–a systematic mapping study, J. Clean. Prod. 244 (2020) 118828.
[28] A.J. Aghbolaghi, et al., Microgrid planning and modeling, Microgrid Architectures, Control and Protection Methods, Springer, 2020, pp. 21–46.
[29] H. Hajebrahimi, et al., A new energy management control method for energy sto- rage systems in microgrids, IEEE Trans. Power Electron. (2020).
[30] AroaR.Mainar,etal.,Anoverviewofprogressinelectrolytesforsecondaryzinc-air batteries and other storage systems based on zinc, J. Energy Storage 15 (2018) 304–328.
[31] M.S. Nazir, et al., Impacts of renewable energy atlas: reaping the benefits of re- newables and biodiversity threats, Int. J. Hydrogen Energy (2020).
[32] M.S. Nazir, et al., Improving the performance of doubly fed induction generator using fault tolerant control—a hierarchical approach, Appl. Sci. 10 (3) (2020) 924.
[33] M.S.Nazir,W.Qi,Impactofsymmetricalshort-circuitfaultondoubly-fedinduction generator controller, Int. J. Electron. (2020).
[34] M. Sedighi, M. Moradzadeh, Impact of demand response program on hybrid renewable energy system planning, Demand Response Application in Smart Grids, Springer, 2020, pp. 215–230.
[35] R. Jadeja, et al., Power quality issues and mitigation techniques in microgrid, Microgrid Architectures, Control and Protection Methods, Springer, 2020, pp. 719–748.
[36] M. Bajaj, A.K. Singh, Grid integrated renewable DG systems: a review of power quality challenges and state‐of‐the‐art mitigation techniques, Int. J. Energy Res. 44 (1) (2020) 26–69.
[37] W. Ahmed, et al., Power quality improving based harmonical studies of a single phase step down bridge-cycloconverter, J. Electr. Syst. 15 (1) (2019) 109–122.
[38] A. Gallo, et al., Energy storage in the energy transition context: a technology re- view, Renew. Sustain. Energy Rev. 65 (2016) 800–822.
[39] M.D. Al-Falahi, S. Jayasinghe, H. Enshaei, A review on recent size optimization methodologies for standalone solar and wind hybrid renewable energy system, Energy Convers. Manag. 143 (2017) 252–274.
[40] M.S. Nazir, et al., Wind generation forecasting methods and proliferation of artifi- cial neural network: a review of five years research trend, Sustainability 12 (9) (2020) 3778.
[41] I. Dincer, C. Acar, A review on clean energy solutions for better sustainability, Int. J. Energy Res. 39 (5) (2015) 585–606.
[42] K. Hussain, M. N. Mohd Salleh, S. Cheng, Y. Shi, Metaheuristic research: a comprehensive survey, Artif. Intelligence Rev., 52 (4), 2191–2233, 10.1007/ s10462-017-9605-z.
[43] S.S.R. M, R. Mallipeddi, K.N. Das, A twin-archive guided decomposition based multi/many-objective evolutionary algorithm, Swarm Evolut. Comp. 71 (2022) 101082, https:
[44] I. Boussaïd, J. Lepagnot, P. Siarry, A survey on optimization metaheuristics, Inf. Sci. 237 (2013) 82–117, https:
[45] T. Dokeroglu, E. Sevinc, T. Kucukyilmaz, A. Cosar, A survey on new generation metaheuristic algorithms, Comp. Ind. Eng. 137 (2019), https:
[46] M.A. Jan, N. Tairan, R. Khanum, W. Mashwani, A new threshold based penalty function embedded MOEA/D, Int. J. Adv. Comp. Sci. Appl. 7 (2016), https:
[47] K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evolut. Comput. 6 (2) (2002) 182– 197, https:
[48] T.P. Runarsson, Y. Xin, Stochastic ranking for constrained evolutionary optimization, IEEE Trans. Evolut. Comput. 4 (3) (2000) 284–294, https:
[49] Z. Fan, W. Li, X. Cai, H. Li, C. Wei, Q. Zhang, K. Deb, E. Goodman, Push and pull search for solving constrained multi-objective optimization problems, Swarm Evolut. Comput. 44 (2019) 665–679.
[50] M. Ming, A. Trivedi, R. Wang, D. Srinivasan, T. Zhang, A dual-population-based evolutionary algorithm for constrained multiobjective optimization, IEEE Trans. Evolut. Comput. 25 (4) (2021) 739–753, https:
[51] I. Das, J.E. Dennis, Normal-boundary intersection: a new method for generating the Pareto surface in nonlinear multicriteria optimization problems, SIAM J. Optimiz. 8 (3) (1998) 631–657.
[52] Y.G. Woldesenbet, G.G. Yen, B.G. Tessema, Constraint handling in multiobjective evolutionary optimization, IEEE Trans. Evolut. Comput. 13 (3) (2009) 514–525, https:
[53] M. A. Jan, Q. Zhang, MOEA/D for constrained multiobjective optimization: Some preliminary experimental results, in 2010 UK Workshop on Computational Intelligence (UKCI), 2010, pp. 1-6, doi: https:
[54] Z. Ma, Y. Wang, W. Song, A new fitness function with two rankings for evolutionary constrained multiobjective optimization, IEEE Trans. Syst., Man, Cybernetics: Syst. 51 (8) (2021) 5005–5016, https:
[55] D. Saxena, T. Ray, K. Deb, A. Tiwari, Constrained many-objective optimization: a way forward. 2009, pp. 545-552, doi: https:
[56] T. Takahama, S. Sakai, Constrained optimization by the e constrained differential evolution with gradient-based mutation and feasible elites, in 2006 IEEE International Conference on Evolutionary Computation, 2006, pp. 1–8, doi: https:
[57] W. Ying, W. He, Y. Huang, D. Li, Y. Wu, An adaptive stochastic ranking mechanism in MOEA/D for constrained multi-objective optimization, in 2016 International Conference on Information System and Artificial Intelligence (ISAI), 2016, pp. 514–518, doi: https:
[58] Z. Fan, Y. Fang, W. Li, X. Cai, C. Wei, E. Goodman, MOEA/D with angle-based constrained dominance principle for constrained multi-objective optimization problems, Appl. Soft Comp. 74 (2019) 621–633, https:
[59] W. Ning, B. Guo, Y. Yan, X. Wu, J. Wu, D. Zhao, Constrained multi-objective optimization using constrained non-dominated sorting combined with an improved hybrid multi-objective evolutionary algorithm, Eng. Optimiz. 49 (10) (2017) 1645–1664, https:
[60] Z.Z. Liu, Y. Wang, Handling constrained multiobjective optimization problems with constraints in both the decision and objective spaces, IEEE Trans. Evol. Comput. 23 (5) (2019) 870–884, https:
[61] Q. Zhu, Q. Zhang, Q. Lin, A constrained multiobjective evolutionary algorithm with detect-and-escape strategy, IEEE Trans. Evolut. Comput. 24 (5) (2020) 938–947, https:
[62] Y. Tian, Y. Zhang, Y. Su, X. Zhang, K.C. Tan, Y. Jin, Balancing objective optimization and constraint satisfaction in constrained evolutionary multiobjective optimization, IEEE Trans. Cybernetics 52 (9) (2022) 9559–9572.
[63] L. Bo, M. Harman, Z. Xuejun, Z. Yan, A memetic co-evolutionary differential evolution algorithm for constrained optimization, in 2007 IEEE Congress on Evolutionary Computation, 2007, pp. 2996-3002, doi: https:
[64] K. Li, R. Chen, G. Fu, X. Yao, Two-archive evolutionary algorithm for constrained multiobjective optimization, IEEE Trans. Evolut. Comput. 23 (2) (2019) 303–315, https:
[65] Y. Tian, T. Zhang, J. Xiao, X. Zhang, Y. Jin, A coevolutionary framework for constrained multiobjective optimization problems, IEEE Trans. Evolut. Comput. 25 (1) (2021) 102–116, https:
[66] F. Ming, W. Gong, H. Zhen, S. Li, L. Wang, Z. Liao, A simple two-stage evolutionary algorithm for constrained multi-objective optimization, KnowledgeBased Syst. 228 (2021), https:
[67] H. Li, Q. Zhang, Multiobjective optimization problems with complicated pareto sets, MOEA/D and NSGA-II, IEEE Transactions on Evolutionary Computation, vol. 13, no. 2, pp. 284-302, 2009. https:
[68] K. Deb, Multi-objective optimisation using evolutionary algorithms: an introduction, in Multi-objective Evolutionary Optimisation for Product Design and Manufacturing, L. Wang, A. H. C. Ng, and K. Deb, Eds. London: Springer London, 2011, pp. 3-34, doi: https:
[69] Z. Liang, K. Hu, X. Ma, Z. Zhu, A many-objective evolutionary algorithm based on a two-round selection strategy, IEEE Trans. Cybernetics 51 (3) (2021) 1417–1429, https:
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