Increased　use　of　environmentally　friendly　and　energy-efficient　transportation,　such　as　electric　vehicles,　results　in　increased　demand　for　power　supply,　putting　pressure　on　the　electricity　infrastructure.　One　of　the　key　challenges　facing　power　companies　operating　within　this　context　is　effectively　planning　power　production　across　multiple　production　technologies.　To　address　this　challenge,　this　study　employs　fuzzy　set　theory　and　proposes　a　combinatorial　optimisation　approach　for　the　problem　of　power　generation　planning.　The　planning　process　is　considered　as　a　multi-period　optimisation　task　where　uncertain　factors　in　each　period　are　considered　as　fuzzy　variables.　The　credibility　expected　value　and　the　lower　semi-variance　in　the　final　production　value　are　considered　as　the　return　and　risk　objectives　of　the　problem,　respectively.　Then,　a　bi-objective　optimisation　model　with　complex　constraints　under　the　influence　of　political　factors　is　proposed.　A　hybrid　intelligent　algorithm　based　on　fuzzy　simulation,　artificial　neural　network　and　multi-objective　genetic　algorithm　is　developed　to　solve　the　model.　A　numerical　example　in　the　Chinese　energy　market　is　tested　to　illustrate　the　effectiveness　of　the　proposed　approach.　The　experimental　results　highlight　seasonal　variation　in　the　profits　and　risks　of　each　production　technology.　From　a　practical　perspective,　the　proposed　approach　can　help　decision-makers　to　establish　multi-period　production　planning.　Additionally,　this　study　analyses　the　optimal　production　planning　for　companies　under　different　levels　of　renewable　energy　and　carbon　emission　standards.　The　results　show　that　the　proposed　approach　can　reflect　the　influence　of　these　policy　factors　on　utilities＇　production　decisions,　which　provides　certain　guidance　for　regulators　to　formulate　appropriate　policies.