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.

The growing share of renewable energy generation based on fluctuating wind and solar energy sources is increasingly challenging in terms of power grid stability. Industrial demand-side response presents a promising way to balance energy supply and consumption. For this, energy demand is flexibly adapted based on external incentives. Thus, companies can economically benefit and at the same time contribute to reducing greenhouse gas emissions. However, there are currently some major obstacles that impede industrial companies from taking part in the energy markets. A broad specification analysis systematically dismantles the existing barriers. On this foundation, a new end-to-end ecosystem of an energy synchronization platform is introduced. It consists of a business-individual company-side platform, where suitable services for energy-oriented manufacturing are offered. In addition, one market-side platform is established as a mediating service broker, which connects the companies to, e.g., third party service providers, energy suppliers, aggregators, and energy markets. The ecosystems aim at preventing vendor lock-in and providing a flexible solution, relying on open standards and offering an integrated solution through an end-to-end energy flexibility data model. In this article, the resulting functionalities are discussed and the remaining deficits outlined.

Kurzfassung: Für den Wirtschaftsstandort Deutschland ist eine stabile Stromversorgung unerlässlich. Die Integration erneuerbarer und volatiler Energiequellen stellt dabei eine große Herausforderung für die Netzstabilität dar. Um die Erzeugung und die Nachfrage zu synchronisieren, können produzierende Unternehmen durch Energieflexibilitätsmaßnahmen ihren Strombedarf gezielt anpassen. Der vorliegende Beitrag zeigt hierzu die möglichen Maßnahmen in den verschiedenen Betriebsebenen einer Produktion auf und verdeutlicht diese anhand eines Beispiels.

Industrial load management plays an important role in the balance of energy consumption and electricity generation, which is increasingly fluctuating due to the growing share of renewable energies. Manufacturing companies are able to adapt their energy consumption by considering energy aspects in their production schedule. It may even be beneficial to temporarily force production resources into idle states and to thus reduce energy demand for a limited period. However, the resulting scheduling problem is very complex and at the same time should be executed in real-time. This paper presents an approach for industrial load management using multi-agent reinforcement learning for energy-oriented rescheduling. A simulation study serves to validate the approach. The results show good solutions and at the same time low computational expense compared to a metaheuristic approach using simulated annealing.

Decarbonisation and the ongoing increase in renewable and unsteady power generation are leading to rising and fluctuating electricity prices. Consequently, companies can decrease energy costs by using production control to flexibly reduce energy demand during periods of high energy costs and vice versa. However, such measures influence the general production objectives and can cause follow-up costs, as for example products may not be finished on time if machines are temporarily shut down to achieve lower energy costs. This paper presents an approach for evaluating the impacts of energy-oriented actions within production control using a cost-model.