Since　the　information　of　the　generation　right　trading　market　is　not　completely　public,　a　trading　individual　need　develop　his　advantageous　bidding　strategies　in　terms　of　limited　information.　This　paper　makes　full　use　of　cellular　automata　(CA)　and　directed　networks　to　model　dynamic　bidding　simulation　for　each　which　participates　in　the　multivariate　generation　right　trading　market　among　wind　farms　and　thermal　units.　Firstly,　it　analyzes　the　evaluation　gains　of　each　trading　individual　in　historical　periods　and　the　risk　gains　of　neighborhood　individuals　in　the　current　period.　Secondly,　it　put　forwards　transfer　matrixes　of　historical　bidding　strategies　and　neighborhood　bidding　strategies　and　forms　the　organic　combination　of　both,　leading　to　a　new　round　bidding　strategy　among　trading　individuals.　Finally,　bidding　simulation　results　are　evaluated　through　R/S　analysis　and　its　Hurst　parameter.　Further,　bidding　strategies　and　risk　sensitivity　are　quantitatively　analyzed　based　on　an　example.　The　results　show　that　this　proposed　method　can　provide　an　effective　and　feasible　bidding　decision　in　the　case　of　asymmetric　information　of　generation　right　trading.　©　2018,　Editorial　Board　of　Journal　of　Systems　Engineering　Society　of　China.　All　right　reserved.