{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T20:41:20Z","timestamp":1779309680887,"version":"3.51.4"},"reference-count":0,"publisher":"Kaunas University of Technology (KTU)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ITC"],"abstract":"<jats:p>To address power imbalance, voltage fluctuations, and\u00a0low new energy consumption\u00a0rate\u00a0caused by high penetration of distributed new energy in multi-power distribution networks, this\u00a0study proposes an energy storage power control method based on an improved ant colony algorithm (ACO).\u00a0First,\u00a0A multi-objective optimization model was constructed, considering the reduction of network loss, improvement of new energy consumption, and stability of energy storage SOC. The entropy weight method was used to objectively\u00a0determine\u00a0the target weights. Second, to solve the poor accuracy of traditional ACO in continuous control, a \"continuous domain discretization\" strategy was introduced, and pheromone update rules were\u00a0optimized\u00a0to enhance algorithm convergence and precision.\u00a0Finally, a case study of a 10kV industrial park distribution network is introduced. The research shows that compared with traditional ant colony algorithm and particle swarm algorithm, the improved algorithm reduces voltage fluctuations by 42%, lowers the daily network loss rate to 2.13% (44.2% lower than no energy storage), and increases the photovoltaic consumption rate on clear days to 96.2%. The results of the paper\u00a0validate\u00a0the effectiveness of the proposed approach in optimizing the operation of the distribution network and promoting the integration of new energy.\u00a0<\/jats:p>","DOI":"10.5755\/j01.itc.55.1.42757","type":"journal-article","created":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T06:41:05Z","timestamp":1775198465000},"page":"54-64","source":"Crossref","is-referenced-by-count":1,"title":["Improved Ant Colony Algorithm for Energy Storage Power Optimization in Multi-Power Distribution Networks"],"prefix":"10.5755","volume":"55","author":[{"given":"Kaipeng","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenwen","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"3786","published-online":{"date-parts":[[2026,4,3]]},"container-title":["Information Technology and Control"],"original-title":[],"link":[{"URL":"https:\/\/itc.ktu.lt\/index.php\/ITC\/article\/download\/42757\/21665","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/itc.ktu.lt\/index.php\/ITC\/article\/download\/42757\/21665","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T06:41:05Z","timestamp":1775198465000},"score":1,"resource":{"primary":{"URL":"https:\/\/itc.ktu.lt\/index.php\/ITC\/article\/view\/42757"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,3]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,3,25]]}},"URL":"https:\/\/doi.org\/10.5755\/j01.itc.55.1.42757","relation":{},"ISSN":["2335-884X","1392-124X"],"issn-type":[{"value":"2335-884X","type":"electronic"},{"value":"1392-124X","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4,3]]}}}