Increasing variability in energy-saving equipment and systems in the greenhouse industry raises the question of how to best utilize the various equipment in such a setting. The development of adequate solutions for deployment and control of this diversity of equipment has not kept pace with the innovations in the greenhouse industry. In earlier work a two-step dynamic optimization framework was developed, where in step one energy demand for heating and cooling is optimized within the climate constraints set by the grower, and in step two energy costs are minimized of alternative equipment use to satisfy that demand. Here the aims are: (1) to develop step two; (2) to illustrate the potential cost savings of both steps by comparing optimization results with real-life data from one specific grower, as a benchmark. The energy equipment of a 4 ha semi-closed greenhouse was optimized on a daily basis using dynamic optimization for a period of one year. Predefined heating, cooling, and electricity demand patterns computed from available grower data served as input, together with realized prices for gas and electricity. The installed equipment contained a boiler, a CHP (combined heat and power installation), short term buffers for high and low temperature heat and cold water storage, a heat pump, an aquifer for long term heat and cold storage and cooling towers. Cooling towers are a new element in the field of greenhouse energy optimization. The results show that cost optimization of the energy system is feasible and beneficial. Energy cost savings of 29% were obtained for the optimized situation as compared to the real situation at the grower. All available equipment was utilized in the optimal situation. The results show that trading of electricity and short-term forecasting of gas and electricity prices in combination with dynamic optimization has a high potential for cost savings in horticultural practice. Dynamic optimization pointed to a higher share of sustainable energy in the energy budget.