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The growing share of renewable power generation leads to increasingly fluctuating and generally rising electricity prices. This is a challenge for industrial companies. However, electricity expenses can be reduced by adapting the energy demand of production processes to the volatile prices on the markets. This approach depicts the new paradigm of energy flexibility to reduce electricity costs. At the same time, using electricity self-generation further offers possibilities for decreasing energy costs. In addition, energy flexibility can be gradually increased by on-site power storage, e.g., stationary batteries. As a consequence, both the electricity demand of the manufacturing system and the supply side, including battery storage, self-generation, and the energy market, need to be controlled in a holistic manner, thus resulting in a smart grid solution for industrial sites. This coordination represents a complex optimization problem, which additionally is highly stochastic due to unforeseen events like machine breakdowns, changing prices, or changing energy availability. This paper presents an approach to controlling a complex system of production resources, battery storage, electricity self-supply, and short-term market trading using multi-agent reinforcement learning (MARL). The results of a case study demonstrate that the developed system can outperform the rule-based reactive control strategy (RCS) frequently used. Although the metaheuristic benchmark based on simulated annealing performs better, MARL enables faster reactions because of the significantly lower computation costs for its own execution.

Power-to-X processes where renewable energy is converted into storable liquids or gases are considered to be one of the key approaches for decarbonizing energy systems and compensating for the volatility involved in generating electricity from renewable sources. In this context, the production of “green” hydrogen and hydrogen-based derivatives is being discussed and tested as a possible solution for the energy-intensive industry sector in particular. Given the sharp, ongoing increases in electricity and gas prices and the need for sustainable energy supplies in production systems, non-energy-intensive companies should also be taken into account when considering possible utilization paths for hydrogen. This work focuses on the following three utilization paths: “hydrogen as an energy storage system that can be reconverted into electricity”, “hydrogen mobility” for company vehicles and “direct hydrogen use”. These three paths are developed, modeled, simulated, and subsequently evaluated in terms of economic and environmental viability. Different photovoltaic system configurations are set up for the tests with nominal power ratings ranging from 300 kWp to 1000 kWp. Each system is assigned an electrolyzer with a power output ranging between 200 kW and 700 kW and a fuel cell with a power output ranging between 5 kW and 75 kW. There are also additional variations in relation to the battery storage systems within these basic configurations. Furthermore, a reference variant without battery storage and hydrogen technologies is simulated for each photovoltaic system size. This means that there are ultimately 16 variants to be simulated for each utilization path. The results show that these utilization paths already constitute a reasonable alternative to fossil fuels in terms of costs in variants with a suitable energy system design. For the “hydrogen as an energy storage system” path, electricity production costs of between 43 and 79 ct/kWh can be achieved with the 750 kWp photovoltaic system. The “hydrogen mobility” is associated with costs of 12 to 15 ct/km, while the “direct hydrogen use” path resulted in costs of 8.2 €/kg. Environmental benefits are achieved in all three paths by replacing the German electricity mix with renewable energy sources produced on site or by substituting hydrogen for fossil fuels. The results confirm that using hydrogen as a storage medium in manufacturing companies could be economically and environmentally viable. These results also form the basis for further studies, e.g., on detailed operating strategies for hydrogen technologies in scenarios involving a combination of multiple utilization paths. The work also presents the simulation-based method developed in this project, which can be transferred to comparable applications in further studies.

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