{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T07:02:52Z","timestamp":1762066972282,"version":"build-2065373602"},"reference-count":21,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T00:00:00Z","timestamp":1663891200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005311","name":"The Science and technology project of China Southern Power Grid Co., Ltd.","doi-asserted-by":"publisher","award":["YNKJXM20220010","YNKJXM20190910","0006200000086476"],"award-info":[{"award-number":["YNKJXM20220010","YNKJXM20190910","0006200000086476"]}],"id":[{"id":"10.13039\/501100005311","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The Technical Transformation Project of Yunnan Power Grid Co., Ltd.","award":["YNKJXM20220010","YNKJXM20190910","0006200000086476"],"award-info":[{"award-number":["YNKJXM20220010","YNKJXM20190910","0006200000086476"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Axioms"],"abstract":"<jats:p>As one of the most important water conservancy projects, reservoirs use water resources to achieve essential functions, such as irrigation, flood control, and power generation, by intercepting rivers. As climate extremes and global warming increase, the world\u2019s water reserves are being tested, and reservoir operators are being challenged. This paper investigates the optimal allocation of shared resources for hydropower to achieve rational decisions for reservoir operations. Firstly, a power resource model is constructed based on the real hydroelectric generator theory. Furthermore, based on the established power resource model combined with the influence of weather type and multi-region heterogeneous demand, this paper constructs a multi-objective hydropower shared resource allocation optimization model, with the lowest hydropower resource supply cost and the shortest time hydropower resource supply time as the optimization objectives. Secondly, for the problem that the traditional population intelligence algorithm easily falls into the local optimum when solving complex problems, the improvement of the MOPSO algorithm is completed by introducing the Levy flight strategy and differential evolution. Finally, simulation experiments were carried out, and cutting-edge algorithms, such as the GA algorithm and WOA algorithm, were selected for simulation comparison to verify the effectiveness of the constructed model and algorithm. The simulation results show that the research in this paper can contribute to effective decision-making for reservoir operators and promote intelligent reservoir operation.<\/jats:p>","DOI":"10.3390\/axioms11100493","type":"journal-article","created":{"date-parts":[[2022,9,25]],"date-time":"2022-09-25T21:14:28Z","timestamp":1664140468000},"page":"493","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Decision Optimization for Water and Electricity Shared Resources Based on Fusion Swarm Intelligence"],"prefix":"10.3390","volume":"11","author":[{"given":"Xiaohua","family":"Yang","sequence":"first","affiliation":[{"name":"Measurement Center, Yunnan Power Grid Co., Ltd., Kunming 650051, China"},{"name":"Key Laboratory of Electric Power Measurement (China Southern Power Grid), Kunming 650217, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Yang","sequence":"additional","affiliation":[{"name":"Measurement Center, Yunnan Power Grid Co., Ltd., Kunming 650051, China"},{"name":"Key Laboratory of Electric Power Measurement (China Southern Power Grid), Kunming 650217, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Bao","sequence":"additional","affiliation":[{"name":"Faculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Shen","sequence":"additional","affiliation":[{"name":"Measurement Center, Yunnan Power Grid Co., Ltd., Kunming 650051, China"},{"name":"Key Laboratory of Electric Power Measurement (China Southern Power Grid), Kunming 650217, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rong","family":"Yan","sequence":"additional","affiliation":[{"name":"Faculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8155-2282","authenticated-orcid":false,"given":"Nan","family":"Pan","sequence":"additional","affiliation":[{"name":"Faculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8847","DOI":"10.1002\/2014GL062308","article-title":"Global warming and changes in risk of concurrent climate extremes: Insights from the 2014 California drought","volume":"41","author":"Kouchak","year":"2014","journal-title":"Geophys. Res. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/j.enpol.2018.11.039","article-title":"Risk evaluation and prevention in hydropower plant operations: A model based on Pythagorean fuzzy AHP","volume":"126","author":"Yucesan","year":"2019","journal-title":"Energy Policy"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1061\/(ASCE)0733-9496(2003)129:3(178)","article-title":"Optimization of Large-scale hydropower system operations","volume":"129","author":"Barros","year":"2003","journal-title":"J. Water Resour. Plan. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1007\/BF01580431","article-title":"A new algorithm for the solution of multi-state dynamic programming problems","volume":"8","author":"Howson","year":"1975","journal-title":"Math. Program."},{"key":"ref_5","unstructured":"Bellman, R.E. (1957). Dynamic Programming, Princeton University Press."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1465","DOI":"10.1007\/s11269-010-9755-0","article-title":"Optimization of multi-reservoir systems by Genetic Algorithm","volume":"25","author":"Ger","year":"2011","journal-title":"Water Resour. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1016\/j.epsr.2009.01.001","article-title":"An improved PSO technique for short-term optimal hydrothermal scheduling","volume":"79","author":"Hota","year":"2009","journal-title":"Electr. Power Syst. Res."},{"key":"ref_8","first-page":"397","article-title":"Application of ant colony algorithm in reservoir optimal operation","volume":"16","author":"Gang","year":"2005","journal-title":"Adv. Water Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Dehghani, M., Riahi-Madvar, H., Hooshyaripor, F., Mosavi, A., Shamshirband, S., Zavadskas, E.K., and Chau, K. (2019). Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System. Energies, 12.","DOI":"10.3390\/en12020289"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"100837","DOI":"10.1109\/ACCESS.2020.2997864","article-title":"Multiobjective Long-Term Generation Scheduling of Cascade Hydroelectricity System Using a Quantum-Behaved Particle Swarm Optimization Based on Decomposition","volume":"8","author":"Hu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"108604","DOI":"10.1016\/j.ress.2022.108604","article-title":"An improved particle swarm optimization algorithm for the reliability\u2013redundancy allocation problem with global reliability","volume":"225","author":"Shuai","year":"2022","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_12","first-page":"1935272","article-title":"An Improved Particle Swarm Optimization Algorithm and Its Application to the Extreme Value Optimization Problem of Multivariable Function","volume":"2022","author":"Min","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yukun, D., Yu, Z., Fubin, L., and Zhengjun, Z. (2022). Research on an Optimization Method for Injection-Production Parameters Based on an Improved Particle Swarm Optimization Algorithm. Energies, 15.","DOI":"10.3390\/en15082889"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yanfang, D., Haoran, M., Hao, W., Junnuo, W., Shuxian, L., Xinyu, L., Jieyu, P., and Qingtai, Q. (2022). Optimal Flood-Control Operation of Cascade Reservoirs Using an Improved Particle Swarm Optimization Algorithm. Water, 14.","DOI":"10.3390\/w14081239"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"012013","DOI":"10.1088\/1742-6596\/2245\/1\/012013","article-title":"An Improved Particle Swarm Optimization Algorithm for Unmanned Aerial Vehicle Route Planning","volume":"2245","author":"Xiaolu","year":"2022","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sharip, Z., Hassan, A.J., and Noh, M. (2019, January 18\u201322). Towards Sustainable Reservoir Management under Future Climate: A Modelling Approach. Proceedings of the 1st International Conference on Dam Safety Management and Engineering, ICDSME, Penang, Malaysia.","DOI":"10.1007\/978-981-15-1971-0_29"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"105858","DOI":"10.1016\/j.enggeo.2020.105858","article-title":"Analysis and Modeling of the Combined Effects of Hydrological Factors on a Reservoir Bank Slope in the Three Gorges Reservoir area, China","volume":"279","author":"Huang","year":"2020","journal-title":"Eng. Geol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Cassano, S., Sossan, F., Landry, C., and Nicolet, C. (2021, January 18\u201321). Performance Assessment of Linear Models of Hydropower Plants. Proceedings of the 2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), Espoo, Finland.","DOI":"10.1109\/ISGTEurope52324.2021.9639912"},{"key":"ref_19","first-page":"1","article-title":"Improved Hydro-Turbine Control and Future Prospects of Variable Speed Hydropower Plant","volume":"99","author":"Kumari","year":"2020","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/s40333-022-0059-z","article-title":"Modeling and analyzing supply-demand relationships of water resources in Xinjiang from a perspective of ecosystem services","volume":"14","author":"Feng","year":"2022","journal-title":"J. Arid. LandIssue"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"104633","DOI":"10.1016\/j.envsoft.2020.104633","article-title":"Smart meters data for modeling and forecasting water demand at the user-level","volume":"125","author":"Pesantezjorge","year":"2020","journal-title":"Environ. Model. Softw."}],"container-title":["Axioms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-1680\/11\/10\/493\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:38:19Z","timestamp":1760143099000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-1680\/11\/10\/493"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,23]]},"references-count":21,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["axioms11100493"],"URL":"https:\/\/doi.org\/10.3390\/axioms11100493","relation":{},"ISSN":["2075-1680"],"issn-type":[{"type":"electronic","value":"2075-1680"}],"subject":[],"published":{"date-parts":[[2022,9,23]]}}}