The　dynamic　transformer　rating　(DTR)　and　thermal　life　loss　of　oil-immersed　transformers　under　on-site　variable　load　Operation　conditions　are　closely　related　to　the　transient　temperature　rise　of　the　equipment.　However,　as　an　implicit　Solution　method,　the　traditional　finite　element　analysis　and　finite　volume　method　need　to　be　iteratively　solved　in　each　sub-step　of　transient　thermal　analysis,　which　has　many　computational　resources　and　is　time　consuming.　It　is　challenging　to　meet　the　requirements　of　fast　calculation.　The　rapid　and　accurate　Solution　of　the　temperature　field　(especially　the　hot　spot　temperature　rise)　of　the　oil-immersed　transformer　in　field　Operation　is　the　premise　to　realize　the　digital　Operation　and　maintenance　of　the　transformer　and　the　DTR　evaluation.　Therefore,　this　paper　proposes　a　lattice　Boltzmann　(LBM)　physical　in　the　loop　Simulation　model　for　coupled　electrical　networks.　The　real　time　evaluation　of　DTR　under　electrical　network　constraints　is　realized　through　the　rapid　Solution　of　the　transformer　temperature　field.Firstly,　the　D2Q9　model　is　used　to　solve　the　fluid　flow　and　thermal　lattice　Boltzmann　equations　(LBEs)　to　capture　the　transient　oil　flow　and　temperature　rise　process　inside　the　transformer.　In　the　Simulink　environment,　the　equivalent　current　source　model　is　used　to　construct　the　electrical　network　constraints　of　multi-level　load　scenarios,　and　the　established　transformer　LBM　model　is　used　as　a　component　for　numerical　encapsulation　to　complete　the　construction　of　the　physical-in-the-loop　Simulation　model.　Secondly,　to　verify　the　effectiveness　of　the　proposed　method,　the　finite　volume　method　(FVM)　is　used　to　simulate　the　same　oil-immersed　transformer　model.　The　grid　independence　test　determines　the　optimal　number　of　grids.　The　number　of　grids　is　1　250x420　for　LBM　modeling　and　Simulation,　and　the　number　of　units　is　43　654　for　FVM　meshing.　Compared　with　the　constructed　LBM-Simulink　model　with　the　FVM　model,　LBM　still　has　the　advantages　of　speed　and　memory　occupation　when　the　number　of　lattices　is　higher　than　the　number　of　FVM　units.　If　commercial　Software　is　used,　this　advantage　will　be　further　expanded.　Thirdly,　the　steady　State　Solution　results　of　the　hot　spot　temperature　rise　of　the　established　LBM　model　are　compared　with　the　FVM　Solution,　and　the　error　is　2.60%.　According　to　the　load　curve　given　in　the　transformer　guidelines,　it　is　used　as　input　to　solve　the　transient　temperature　rise　of　the　established　LBM　and　FVM　Simulation　models.　Finally,　the　results　show　that　the　LBM　and　FVM　calculations　are　better　than　the　transformer　guide　calculation.　The　maximum　error　between　the　hot　spot　temperature　calculated　by　LBM　and　FVM　is　6.44%.　Moreover,　the　hot　spot　temperature　rise　trend　of　LBM　is　consistent　with　the　transformer　load　guidelines,　which　verifies　the　effectiveness　of　the　proposed　method.Based　on　the　constructed　LBM　model,　the　load　capacity　of　oil-immersed　transformers　under　constant　25　°C　and　typical　ambient　temperature　changes　in　summer　and　winter　are　evaluated　at　6~　18　hours　during　the　day.　The　results　show　that　under　the　premise　that　the　relative　insulation　life　loss　of　oil-immersed　transformers　is　less　than　1.　The　maximum　load　capacity　coefficients　are　1.20,　1.10,　and　1.60　under　the　constant　ambient　temperature　of　25　°C,　typical　temperature　changes　in　summer,　and　typical　temperature　changes　in　winter.　The　Simulation　model　based　on　the　proposed　LBM　provides　an　effective　method　for　real-time　monitoring　of　temperature　rise,　load　capacity　evaluation,　and　dynamic　capacity　increase　of　oil-immersed　transformers.　©　2025　China　Machine　Press.　All　rights　reserved.