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This research introduces specific scheduling schemes tailored to various time scales within distribution networks, including a fuzzy optimized day ahead scheduling scheme, an intra-day scheduling scheme combined with Deep Q Network, and an adaptive optimized real-time scheduling scheme. This plan mainly considers the impact of photovoltaic output and conducts scheduling one day in advance through fuzzy optimization. In the intraday scheduling, different strategies were adopted in the study. By combining with Deep Q Network, research on scheduling for intraday demand within the power system. The analysis is conducted through rigorous modeling. Experimental tests were conducted to evaluate the performance of the proposed schemes. The day ahead dispatching primarily considers the impact of photovoltaic output and calculates the cost associated with each link in the grid under three different meteorological conditions. In the intra-day scheduling, the total costs for Scenario 1, Scenario 2, and Scenario 3 are found to be 34,724.5 yuan, 36,296.5 yuan, and 33,275.8 yuan, respectively. Notably, strategies 1 and 2 demonstrate lower costs compared to the pre-day scheduling, with the exception of Scenario 3. In real-time scheduling, considering the matching between sources and sources, the matching rate between sources and sources can be maintained at over 95%, and the stability and cost of the power grid have significantly decreased. In summary, by proposing a multi-stage time scale economic scheduling scheme, this study fully considers the uncertainty of the power supply of the distribution network access, as well as the different needs of day, day and real-time scheduling, providing an effective solution for the power dispatching of virtual power plants and providing important technical support for the reliability and economy of the power system.<\/jats:p>","DOI":"10.3233\/jcm-247299","type":"journal-article","created":{"date-parts":[[2024,5,10]],"date-time":"2024-05-10T11:45:49Z","timestamp":1715341549000},"page":"935-953","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["Multi-time scale economic regulation model of virtual power plant considering multiple uncertainties of source, load and storag"],"prefix":"10.66113","volume":"24","author":[{"given":"Zhenlan","family":"Dou","sequence":"first","affiliation":[{"name":"State Grid Shanghai Municipal Electric Power Company","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunyan","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Grid Shanghai Municipal Electric Power Company","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanxu","family":"Duan","sequence":"additional","affiliation":[{"name":"Nanchang University","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan","family":"Wen","sequence":"additional","affiliation":[{"name":"Nanchang University","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Sun","sequence":"additional","affiliation":[{"name":"State Grid Shanghai Municipal Electric Power Company","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"55691","published-online":{"date-parts":[[2024,5]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2020.3026971"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2023.121416"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10614-017-9716-2"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSTE.2022.3187217"},{"key":"e_1_3_2_6_2","doi-asserted-by":"crossref","unstructured":"LiS WuW LiY Robust data-driven and fully distributed volt\/var control for active distribution networks with multiple virtual power plants. 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