Development of an Energy Planning Model Using Temporal Production Simulation and Enhanced NSGA-III

Authors

  • Xiaojun Li Information Data Department Guangdong Electric Power Trading Center Co. Ltd., Guangzhou, Guangdong 510000, China
  • Yilong Ni Information Data Department Guangdong Electric Power Trading Center Co. Ltd., Guangzhou, Guangdong 510000, China
  • Shuo Yang Information Data Department Guangdong Electric Power Trading Center Co. Ltd., Guangzhou, Guangdong 510000, China
  • Zhuocheng Feng Information Data Department Guangdong Electric Power Trading Center Co. Ltd., Guangzhou, Guangdong 510000, China
  • Qiang Liu Information Data Department Guangdong Electric Power Trading Center Co. Ltd., Guangzhou, Guangdong 510000, China
  • Jian Qiu Information Data Department Guangdong Electric Power Trading Center Co. Ltd., Guangzhou, Guangdong 510000, China
  • Chao Zhang Information Data Department Guangdong Electric Power Trading Center Co. Ltd., Guangzhou, Guangdong 510000, China

DOI:

https://doi.org/10.4108/ew.5721

Abstract

This paper presents an innovative model of Energy Planning Model which allows navigating the complexities of modern energy systems. Our model utilizes a combination of Temporal Production Simulation and an Enhanced Non-Dominated Sorting Genetic Algorithm III to address the challenge associated with fluctuating energy demands and renewable sources integration. The model represents a significant advancement in energy planning due to its capacity to simulate energy production and consumption dynamics over time. The unique feature of the model is based on Temporal Production Simulation, meaning that the model is capable of accounting for hourly, daily, and seasonal fluctuations in energy supply and demand. Such temporal sensitivity is crucial for optimization in systems with high percentages of intermittent renewable sources, as existing planning solutions largely ignore such fluctuations. Another component of the model is the Enhanced NSGA-III algorithm that is uniquely tailored for the nature of multi-objective energy planning where one must balance their cost, environmental performance, and reliability. We have developed improvements to NSGAIII to enhance its efficiency when navigating the complex decision space associated with energy planning to reach faster convergence and to explore more optimal solutions. Methodologically, we use a combination of in-depth problem definition approach, advanced simulation, and algorithmic adjustments. We have validated our model against existing models and testing it in various scenarios to illustrate its superior ability to reach optimal energy plans based on efficiency, sustainability, and reliability under various conditions. Overall, through its unique incorporation of the Temporal Production Simulation and an improved optimization algorithm, the Energy Planning Model provides novel insights and practical decision support for policymakers and energy planners developed to reach the optimal sustainable solutions required for the high penetration of renewables.

Downloads

Download data is not yet available.

References

J. Yu, C. Sun, R. Kong, and Z. Zhao, "Multiobjective optimization configuration of wind-solarstorage microgrid based on NSGA-III," J. Phys. Conf. Ser., vol. 2005, no. 1, p. 012149, Aug. 2021, doi: 10.1088/1742-6596/2005/1/012149. DOI: https://doi.org/10.1088/1742-6596/2005/1/012149

Y. Chi, M. Yang, R. Zhang, and X. Peng, "Multi-Objective Joint Planning Method of Distributed Photovoltaic and

Battery Energy Storage System Based on NSGA-III Algorithm," in 2022 IEEE 6th Conf. on Energy Internet and Energy System Integration (EI2), IEEE, Nov. 2022, pp. 940–945. doi: 10.1109/EI256261.2022.10116136. DOI: https://doi.org/10.1109/EI256261.2022.10116136

T. T. Teo et al., "Optimization of Fuzzy EnergyManagement System for Grid-Connected Microgrid Using NSGA-II," IEEE Trans. Cybern., vol.

, no. 11, pp. 5375–5386, Nov. 2021, doi:

1109/TCYB.2020.3031109.

M. Elarbi, S. Bechikh, and L. Ben Said, "On the importance of isolated infeasible solutions in the many-objective constrained NSGA-III," Knowl.

Based Syst., vol. 227, p. 104335, Sep. 2021, doi: DOI: https://doi.org/10.1016/j.knosys.2018.05.015

1016/j.knosys.2018.05.015. DOI: https://doi.org/10.1088/1475-7516/2018/05/015

A. Tiwari, K. Sharma, and M. K. Trivedi, "NSGA-

III-Based Time–Cost–Environmental Impact Trade-Off Optimization Model for Construction Projects," 2022, pp. 11–25. doi: 10.1007/978-981-16-1220-6_2. DOI: https://doi.org/10.1007/978-981-16-1220-6_2

W. Peng, J. Lin, J. Zhang, and L. Chen, "A biobjective hierarchical program scheduling problem and its solution based on NSGA-III," Ann. Oper. Res., vol. 308, no. 1–2, pp. 389–414, Jan. 2022, doi: 10.1007/s10479021-04106-z. DOI: https://doi.org/10.1007/s10479-021-04106-z

Z. Cui, Y. Chang, J. Zhang, X. Cai, and W. Zhang, "Improved NSGA-III with selection-and-elimination operator," Swarm Evol. Comput., vol. 49, pp. 23–33, Sep. 2019, doi: 10.1016/j.swevo.2019.05.011. DOI: https://doi.org/10.1016/j.swevo.2019.05.011

X. Wu, J. Li, X. Shen, and N. Zhao, "NSGA-III for solving dynamic flexible job shop scheduling problem considering deterioration effect," IET Collaborative Intelligent Manufacturing, vol. 2, no. 1, pp. 22–33, Mar. 2020, doi: 10.1049/iet-cim.2019.0056. DOI: https://doi.org/10.1049/iet-cim.2019.0056

S. Moreno, J. Ortega, M. Damas, A. Díaz, J. González, and H. Pomares, "Prediction of energy consumption in a NSGA-II-based evolutionary algorithm," in Proc. of the Genetic and Evolutionary Computation Conference Companion, New York, NY, USA: ACM, Jul. 2018, pp. 239–240. doi: 10.1145/3205651.3205766. DOI: https://doi.org/10.1145/3205651.3205766

B. N. CHEBOUBA, M. A. MELLAL, S. ADJERID, and D. BENAZZOUZ, "System Reliability and Cost Optimization Under Various Scenarios Using NSGA-III," in 2020 International Conf. on Electrical Engineering (ICEE), IEEE, Sep. 2020, pp. 1–6. doi: 10.1109/ICEE49691.2020.9249929. DOI: https://doi.org/10.1109/ICEE49691.2020.9249929

A. A. Farahani and S. H. H. Sadeghi, "The Use of NSGA II for Optimal Placement and Management of Renewable Energy Sources When Considering Network Uncertainty and Fault Current Limiters," in 2021 29th Iranian Conf. DOI: https://doi.org/10.1109/ICEE52715.2021.9544336

on Electrical Engineering (ICEE), IEEE, May 2021, pp. 437–442. doi: 10.1109/ICEE52626.2021.9514534.

H. Wang, Y. Jin, and X. Yao, "Diversity assessment strategies for multiobjective and many-objective optimization: A survey and analysis," IEEE Trans. Cybern., vol. 50, no. 8, pp. 3448–3461, Aug. 2020, doi:

1109/TCYB.2019.2913952.

L. Li, L. L. Lai, X. Li, and Z. Yi, "A Comprehensive Review of NSGA-Based Evolutionary Algorithms for Multi-Objective and Many-Objective Optimization," Algorithms, vol. 14, no. 1, p. 32, Jan. 2021, doi: 10.3390/a14010032.

M. A. Awadallah, B. Venkatesh, and V. S. Asirvadam, "An Enhanced NSGA-II Algorithm for MultiObjective Problems With Complex Constraints," IEEE Access, vol. 7, pp. 116402–116415, 2019, doi: 10.1109/ACCESS.2019.2935861.

S. R. Hejazi, A. Memariani, A. Jahanshahloo, and G. R. Jahanshahloo, "Extending NSGA-II to solve fuzzy multi-objective decision making problems," Decision

Sci. Lett., vol. 9, no. 1, pp. 15–32, 2020, doi: DOI: https://doi.org/10.1109/LPT.2020.3011788

5267/j.dsl.2019.5.003.

L. Davis, Handbook of Genetic Algorithms. New York, NY: Van Nostrand Reinhold, 1991.

K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-II," IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182–197, Apr. 2002, doi: 10.1109/4235.996017. DOI: https://doi.org/10.1109/4235.996017

K. Sindhya, K. Deb, and K. Miettinen, "A Local Search Based Evolutionary Multi-Objective Optimization Algorithm for Fast and Accurate Convergence," in Parallel Problem Solving from Nature - PPSN X, Springer, Berlin, Heidelberg, 2008, pp. 815–824. doi: 10.1007/978-3-54087700-481. DOI: https://doi.org/10.1007/978-3-540-87700-4_81

J. D. Schaffer, "Multiple objective optimization with vector evaluated genetic algorithms," in Proceedings of the 1st International Conference on Genetic Algorithms, Hillsdale, NJ, USA: L. Erlbaum Associates Inc., 1985, pp. 93–100.

D. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1989.

E. Zitzler, M. Laumanns, and L. Thiele, "SPEA2: Improving the strength pareto evolutionary algorithm," TIK-report, vol. 103, 2001.

Q. Zhang and H. Li, "MOEA/D: A multiobjective evolutionary algorithm based on decomposition," IEEE Trans. Evol. Comput., vol. 11, no. 6, pp. 712–731, Dec. 2007, doi: 10.1109/TEVC.2007.892759. DOI: https://doi.org/10.1109/TEVC.2007.892759

H. Ishibuchi, H. Masuda, Y. Tanigaki, and Y. Nojima, "Difficulty in evolutionary multiobjective optimization of noisy problems," IEEE Access, vol. 3, pp. 722–734, 2015, doi: 10.1109/ACCESS.2015.2443115.

K. Deb and H. Jain, "An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints," IEEE Trans. Evol. Comput., vol. 18, no. 4, pp. 577–601, Aug. 2014, doi: 10.1109/TEVC.2013.2281535. DOI: https://doi.org/10.1109/TEVC.2013.2281535

D. A. Van Veldhuizen and G. B. Lamont, "Multiobjective evolutionary algorithm research: A history and analysis," Technical Report TR-98-03, Department of Electrical and Computer Engineering, Air Force Institute of Technology, Wright-Patterson AFB, OH, 1998.

C. A. Coello Coello, G. B. Lamont, and D. A. Van

Veldhuizen, Evolutionary Algorithms for Solving MultiObjective Problems. New York, NY, USA: Genetic and Evolutionary Computation, 2007.

A. E. Eiben and J. E. Smith, Introduction to Evolutionary Computing. Berlin, Heidelberg: Springer-Verlag, 2003. DOI: https://doi.org/10.1007/978-3-662-05094-1

L. Davis, Genetic Algorithms and Genetic Programming: Theory and Applications. Norwell, MA, USA: Kluwer Academic Publishers, 1996.

K. Miettinen, Nonlinear Multiobjective Optimization. Boston, MA: Kluwer Academic Publishers, 1999.

M. Ehrgott, Multicriteria Optimization. Berlin, Heidelberg: Springer-Verlag, 2005.

Downloads

Published

10-04-2024

How to Cite

1.
Li X, Ni Y, Yang S, Feng Z, Liu Q, Qiu J, Zhang C. Development of an Energy Planning Model Using Temporal Production Simulation and Enhanced NSGA-III. EAI Endorsed Trans Energy Web [Internet]. 2024 Apr. 10 [cited 2024 May 20];11. Available from: https://publications.eai.eu/index.php/ew/article/view/5721