Batch　processing　machines,　which　operate　multiple　jobs　at　a　time,　are　commonly　used　in　energy-intensive　industries.　A　significant　amount　of　energy　can　be　saved　in　such　industries　using　production　scheduling　as　an　approach　to　enhance　efficiency.　This　study　deals　with　an　energy-aware　scheduling　problem　for　parallel　batch　processing　machines　with　incompatible　families　and　job　release　times.　In　such　an　environment,　a　machine　may　need　to　wait　until　all　the　jobs　in　the　next　batch　become　ready.　During　waiting　time,　a　machine　can　be　switched　off　or　kept　on　standby　for　more　energy-efficient　scheduling.　We　first　present　a　mixed-integer　linear　programming　(MILP)　model　to　solve　the　problem.　However,　the　presented　MILP　model　can　only　solve　small　problem　instances.　We　therefore　propose　an　energy-efficient　tabu　search　(ETS)　algorithm　for　solving　larger　problem　instances.　The　proposed　solution　framework　incorporates　multiple　neighborhood　methods　for　efficient　exploration　of　the　search　space.　An　energy-related　heuristic　is　also　integrated　into　the　ETS　for　minimizing　energy　consumption　during　the　waiting　time.　The　performance　of　our　proposed　ETS　algorithm　is　validated　by　comparing　it　with　CPLEX　for　small　problem　instances　and　with　two　other　heuristic　algorithms　for　larger　problem　instances.　The　contribution　of　different　components　in　ETS　is　also　established　in　our　experimental　studies.　The　proposed　solution　framework　is　expected　to　bring　many　benefits　in　energy-intensive　industries　both　economically　and　environmentally.