The share of Renewable Energy Sources (RES) has increased significantly over the last two decades. In comparison to traditional energy sources, the output of RES like wind and solar power is highly variable. As a result, energy prices vary considerably over short periods of time. Both rising and more volatile energy prices are strong incentives for manufacturing companies to become more energy-effcient and fexible. A promising approach is the intelligent control of Industrial Energy Supply Systems (IESS), which provide various energy services to industrial production facilities and machines. Due to the high complexity of such systems, consisting of interacting energy converters, grids and thermal as well as electrical storages, widespread conventional control approaches often lead to suboptimal operating behavior and limited flexibility. Rising digitization in industrial production sites offers the opportunity to implement new advanced algorithms such as Mixed Integer Linear Programming (MILP) or Deep Reinforcement Learning (DRL) to optimize the operational strategies of IESS and thereby enable an effective Demand Side Management. This paper presents a comparative study of different controllers for optimized operation strategies. For this purpose, a framework is used that allows for a standardized comparison of rule-, model- and data-based controllers by connecting them to dynamic simulation models of IESS of varying complexity. The results indicate that DRL and MILP controllers have a huge potential to reduce energy-related cost of up to 50 % for less complex and around 6 % for more complex systems. In some cases however, both algorithms still show unfavorable operating behavior in terms of non-direct costs such as temperature and switching restrictions, depending on the complexity and general conditions of the systems.