The urgent need for industrial efficient solutions allowing to reduce the environmental footprint of modern man-made systems is nowadays acknowledged by all and becomes a central issue. This reality is all the more true for cyber-physical systems where the integrations of computation, networking, and physical processes are intensively realized. These intelligent systems which combine several computing resources interacting with the physical environment using sensors and actuators are known to be energy-intensive. So, the management of the energy becomes one of the major concerns driven by the increasing number of connected systems. This paper addresses the challenge of estimating least-cost firing sequences within P-Time Labeled Petri Net systems, incorporating both energy considerations and temporal constraints into the modeling framework. By extending the existing models with a dynamic programming algorithm, the proposed method calculates optimal sequences that minimize energy consumption for a given set of actions over time. An industrial example is presented to illustrate the practical application of the approach, demonstrating the potential for significant energy savings and efficiency improvements in industrial systems.