{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T20:34:48Z","timestamp":1776976488994,"version":"3.51.4"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>Computational efficiency, Data privacy, and equitable benefit assessment are some of the issues that have arisen as a result of the fast expansion of distributed energy resources (DERs), which have added complexity to the functioning of distribution networks. This paper presents a two-tiered VPP coordination architecture that takes into account the operational interests of both Distribution System Operators (DSOs) or VPPs under AC optimum power flow (AC-OPF) limitations. The goal is to solve these challenges. A penalty-function-enhanced OPF mechanism is used in the upper layer to guarantee network security in the event of voltage or branch-limit violations, and an Asynchronous Advantage Actor-Critic (A3C) multi-agent architecture is integrated into the lower layer to utilize a parameter-sharing Twin-Delayed Deep Deterministic Policy Gradient (PS-TD3) algorithm. Through lightweight parameter sharing and decentralized execution, every agent\u2014 which represents a VPP subsystem\u2014learns optimum judgments for energy-dispatch, storage, and flexibility, resulting in dramatically reduced computational cost and preservation of data privacy. When compared to the non-cooperative TD3 baseline, traditional distributed OPF, and independent Q-learning, the suggested dual-layer MARL approach outperforms all three in simulation tests conducted on the IEEE 33-node distribution network. Thanks to parameter sharing, the PS-TD3 + A3C hybrid improves convergence speed through 42% and reduces per-step computing time by 37%. It also reduces voltage variation by 31.4%, network real-power losses by 26.7%, and operating cost by 18.2%. Since agents only share compressed gradients and not raw operational data, privacy leakage is minimized by more than 80%. In contemporary distribution systems that are rich in distributed energy resources (DERs), the findings show that the suggested framework provides a computationally efficient, scalable, and privacy-preserving approach for coordinated VPP operation.<\/jats:p>","DOI":"10.31449\/inf.v50i11.12252","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T19:36:18Z","timestamp":1776972978000},"source":"Crossref","is-referenced-by-count":0,"title":["Hierarchical Multi-Agent Deep Reinforcement Learning for Coordinated Optimization of Aggregated Virtual Power Plants in Smart Microgrids"],"prefix":"10.31449","volume":"50","author":[{"given":"Junxiong","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qihang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,4,23]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/12252\/6655","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/12252\/6655","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T19:38:39Z","timestamp":1776973119000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/12252"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,23]]},"references-count":0,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2026,4,23]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i11.12252","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4,23]]}}}