{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:35:26Z","timestamp":1760060126935,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T00:00:00Z","timestamp":1754179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Project of State Grid Shandong Electric Power Company","award":["520625240005"],"award-info":[{"award-number":["520625240005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>A high-quality set of typical scenarios is significant for power grid planning. Existing construction methods for typical scenarios do not account for the spatiotemporal correlations among renewable energy plant outputs, failing to adequately reflect the distribution characteristics of original scenarios. To address the issues mentioned above, this paper proposes a construction method for typical scenarios considering spatiotemporal correlations, providing high-quality typical scenarios for power grid planning. Firstly, a symmetric spatial correlation matrix and a temporal autocorrelation matrix are defined, achieving quantitative representation of spatiotemporal correlations. Then, distributional differences between typical and original scenarios are quantified from multiple dimensions, and a scenario reduction model considering spatiotemporal correlations is established. Finally, the genetic algorithm is improved by incorporating adaptive parameter adjustment and an elitism strategy, which can efficiently obtain high-quality typical scenarios. A case study involving five wind farms and fourteen photovoltaic plants in Belgium is presented. The rate of error between spatial correlation matrices of original and typical scenario sets is lower than 2.6%, and the rate of error between temporal autocorrelations is lower than 2.8%. Simulation results demonstrate that typical scenarios can capture the spatiotemporal correlations of original scenarios.<\/jats:p>","DOI":"10.3390\/sym17081226","type":"journal-article","created":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T09:41:17Z","timestamp":1754300477000},"page":"1226","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Construction of Typical Scenarios for Multiple Renewable Energy Plant Outputs Considering Spatiotemporal Correlations"],"prefix":"10.3390","volume":"17","author":[{"given":"Yuyue","family":"Zhang","sequence":"first","affiliation":[{"name":"Economic & Technology Research Institute of State Grid Shandong Electric Power Company, Jinan 250021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Wen","sequence":"additional","affiliation":[{"name":"Economic & Technology Research Institute of State Grid Shandong Electric Power Company, Jinan 250021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nan","family":"Wang","sequence":"additional","affiliation":[{"name":"Economic & Technology Research Institute of State Grid Shandong Electric Power Company, Jinan 250021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenhua","family":"Yuan","sequence":"additional","affiliation":[{"name":"Economic & Technology Research Institute of State Grid Shandong Electric Power Company, Jinan 250021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lina","family":"Zhang","sequence":"additional","affiliation":[{"name":"Economic & Technology Research Institute of State Grid Shandong Electric Power Company, Jinan 250021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Runjia","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Shandong University, Jinan 250061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100030","DOI":"10.1016\/j.energ.2025.100030","article-title":"Exploring trends and predictions in renewable energy generation","volume":"4","author":"Khaleel","year":"2025","journal-title":"Energy 360"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3681","DOI":"10.1109\/TSG.2023.3236724","article-title":"Distributed online voltage control with fast PV power fluctuations and imperfect communication","volume":"14","author":"Wang","year":"2023","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"121067","DOI":"10.1109\/ACCESS.2019.2936936","article-title":"Multistage active distribution network planning with restricted operation scenario selection","volume":"7","author":"Zhao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"107722","DOI":"10.1016\/j.epsr.2021.107722","article-title":"A review of scenario analysis methods in planning and operation of modern power systems: Methodologies, applications, and challenges","volume":"205","author":"Li","year":"2022","journal-title":"Electr. Power Syst. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3040","DOI":"10.1109\/TPWRS.2020.2965922","article-title":"A class-driven approach based on long short-term memory networks for electricity price scenario generation and reduction","volume":"35","author":"Stappers","year":"2020","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1749","DOI":"10.1049\/iet-rpg.2017.0278","article-title":"Stochastic multi-objective framework for optimal dynamic planning of interconnected microgrids","volume":"11","author":"Gazijahani","year":"2017","journal-title":"IET Renew. Power Gener."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2368","DOI":"10.1109\/TPWRS.2012.2204409","article-title":"Compromising wind and solar energies from the power system adequacy viewpoint","volume":"27","author":"Safdarian","year":"2012","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3265","DOI":"10.1109\/TPWRS.2018.2794541","article-title":"Model-free renewable scenario generation using generative adversarial networks","volume":"33","author":"Chen","year":"2018","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1109\/TPWRS.2022.3170992","article-title":"Conditional style-based generative adversarial networks for renewable scenario generation","volume":"38","author":"Yuan","year":"2022","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1109\/JETCAS.2023.3291145","article-title":"Generation method of multi-regional photovoltaic output scenarios-set using conditional generative adversarial networks","volume":"13","author":"Song","year":"2023","journal-title":"IEEE J. Emerg. Sel. Top. Circuits Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2630","DOI":"10.1109\/TPWRS.2023.3277698","article-title":"A cross-modal generative adversarial network for scenarios generation of renewable energy","volume":"39","author":"Kang","year":"2024","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"118387","DOI":"10.1016\/j.apenergy.2021.118387","article-title":"Data-driven scenario generation of renewable energy production based on controllable generative adversarial networks with interpretability","volume":"308","author":"Dong","year":"2022","journal-title":"Appl. Energy"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yao, W., Huo, Z., Zou, J., Wu, C., Wang, J., Wang, X., Lu, S., Xie, Y., Zhuo, Y., and Liang, J. (2024). Medium- and long-term power system planning method based on source-load uncertainty modeling. Energies, 17.","DOI":"10.3390\/en17205088"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"9323","DOI":"10.1002\/er.7808","article-title":"Multipoint layout planning method for multi-energy sources based on time-series production simulation","volume":"46","author":"Suo","year":"2022","journal-title":"Int. J. Energy Res."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Liu, D. (2021, January 26\u201329). An improved wind power scenario reduction method considering spatial-temporal correlation and cyber attacks. Proceedings of the 3rd Asia Energy and Electrical Engineering Symposium (AEEES 2021), Chengdu, China.","DOI":"10.1109\/AEEES51875.2021.9403198"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"125747","DOI":"10.1016\/j.apenergy.2025.125747","article-title":"Extreme day-ahead renewables scenario selection in power grid operations","volume":"391","author":"Ludkovski","year":"2025","journal-title":"Appl. Energy"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.compchemeng.2016.02.005","article-title":"Linear programming-based scenario reduction using transportation distance","volume":"88","author":"Li","year":"2016","journal-title":"Comput. Chem. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1109\/TIA.2019.2951118","article-title":"Multi-scenario based Bi-level coordinated planning of active distribution system under uncertain environment","volume":"56","author":"Sannigrahi","year":"2019","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5162","DOI":"10.1016\/j.ijhydene.2018.09.179","article-title":"A scenario generation method based on the mixture vine copula and its application in the power system with wind\/hydrogen production","volume":"44","author":"Qiu","year":"2019","journal-title":"Int. J. Hydrogen Energy"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.ijepes.2014.05.071","article-title":"Scenario selection in composite reliability assessment of deregulated power systems","volume":"63","author":"Kile","year":"2014","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1657","DOI":"10.1109\/TPWRS.2015.2412687","article-title":"A scenario optimal reduction method for wind power time series","volume":"31","author":"Li","year":"2016","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhou, H., Wu, H., Ye, C., Xiao, S., Zhang, J., He, X., and Wang, B. (2019). Integration capability evaluation of wind and photovoltaic generation in power systems based on temporal and spatial correlations. Energies, 12.","DOI":"10.3390\/en12010171"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.apenergy.2018.03.082","article-title":"Efficient scenario generation of multiple renewable power plants considering spatial and temporal correlations","volume":"221","author":"Tang","year":"2018","journal-title":"Appl. Energy"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1936","DOI":"10.1109\/TPWRS.2016.2596803","article-title":"Selecting representative days for capturing the implications of integrating intermittent renewables in generation expansion planning problems","volume":"32","author":"Poncelet","year":"2017","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"123194","DOI":"10.1016\/j.apenergy.2024.123194","article-title":"GGNet: A novel graph structure for power forecasting in renewable power plants considering temporal lead-lag correlations","volume":"364","author":"Zhu","year":"2024","journal-title":"Appl. Energy"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1298","DOI":"10.1109\/TSTE.2017.2782089","article-title":"Generation of time-coupled wind power infeed scenarios using pair-copula construction","volume":"9","author":"Raik","year":"2018","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sharma, K., Bhakar, R., Tiwari, H.P., and Chawda, S. (2017, January 5\u20137). Scenario based uncertainty modeling of electricity market prices. Proceedings of the 6th International Conference on Computer Applications in Electrical Engineering-Recent Advances (CERA 2017), Roorkee, India.","DOI":"10.1109\/CERA.2017.8343320"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"108813","DOI":"10.1016\/j.epsr.2022.108813","article-title":"Reduction method for multi-period time series scenarios of wind power","volume":"214","author":"He","year":"2023","journal-title":"Electr. Power Syst. Res."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Cheng, D., Xing, F., Su, R., Qi, H., Ma, L., Ma, H., Wang, T., Zhou, N., and Li, C. (2023, January 22\u201324). Multiple wind farms power generation scenario generation and reduction based on multivariate copula function and greedy strategy. Proceedings of the 7th International Conference on Power and Energy Engineering (ICPEE 2023), Chengdu, China.","DOI":"10.1109\/ICPEE60001.2023.10453696"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"110908","DOI":"10.1016\/j.asoc.2023.110908","article-title":"A review of metaheuristic algorithms for solving TSP-based scheduling optimization problems","volume":"148","author":"Toaza","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"118207","DOI":"10.1016\/j.enconman.2024.118207","article-title":"A review of the applications of artificial intelligence in renewable energy systems: An approach-based study","volume":"306","author":"Shoaei","year":"2024","journal-title":"Energy Convers. Manag."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"8091","DOI":"10.1007\/s11042-020-10139-6","article-title":"A review on genetic algorithm: Past, present, and future","volume":"80","author":"Katoch","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2759","DOI":"10.1109\/JSYST.2019.2931934","article-title":"Multi-objective multi-scenario under-frequency load shedding in a standalone power system","volume":"14","author":"Hong","year":"2020","journal-title":"IEEE Syst. J."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1109\/TEVC.2014.2360890","article-title":"Differential evolution with an individual-dependent mechanism","volume":"19","author":"Tang","year":"2015","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_35","unstructured":"Pandey, A.K., Jadoun, V.K., and Jayalakshmi, N.S. (2022, January 25\u201327). Profit maximization based optimal scheduling of a virtual power plant using red fox optimizer. Proceedings of the IEEE 10th Power India International Conference (PIICON 2022), New Delhi, India."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Pandey, A.K., Jadoun, V.K., Sabhahit, J.N., and Sharma, S. (2025). Interconnected operation and economic feasibility-based sustainable planning of virtual power plant in multi-area context. Smart Cities, 8.","DOI":"10.3390\/smartcities8010037"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/8\/1226\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:22:13Z","timestamp":1760034133000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/8\/1226"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,3]]},"references-count":36,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["sym17081226"],"URL":"https:\/\/doi.org\/10.3390\/sym17081226","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2025,8,3]]}}}