Ever-changing variables in the electricity market require energy management systems to make optimal real-time decisions adaptively. Demand response is the latest approach being used to accelerate the efficiency and stability of power systems. This paper proposes an hour-ahead demand response algorithm for home energy management systems. To deal with the uncertainty in future prices, a steady price prediction model based on artificial neural network is presented. In cooperation with forecasted future prices, multi-agent reinforcement learning is adopted to make optimal decisions for different home appliances in a decentralized manner. To verify the performance of the proposed energy management scheme, simulations are conducted with non-shiftable, shiftable, and controllable loads. Experimental results demonstrate that the proposed demand response algorithm can handle energy management for multiple appliances, minimize user energy bills and dissatisfaction costs, and help the user to significantly reduce its electricity cost compared with a benchmark without demand response.

Demand response (DR) has been recognized as a powerful tool to help mitigate power imbalances in a future smart energy system. This paper takes the point of view of a grid operator (GO) to establish an intraday resource trading framework, over which DR resources from different sectors (large industrial consumers and small- or middle-sized customers) are brought to the system level and are assessed by the GO along with the generators, with the aim of procuring the required system resources at minimal cost. Considering the distinct behaviors of the involved entities, Stackelberg game theory was adopted to analyze the coordination among the various decision makers. A unique Stackelberg equilibrium was identified that yields the optimal outcome for the resource trading game between the GO and demand-side participators. Simulation results demonstrated that the total procurement cost was minimized by applying the proposed approach.

Diverse customer demands are increasing the need for improved flexibility, adaptability, and transparency of production processes; traditional manufacturing systems must change. To meet these requirements, cyberphysical system (CPS) technologies are being increasingly applied in industrial manufacturing, equipping production facilities with autonomous adaptation capabilities. This is achieved by incorporating physical objects and their virtual representations and enabling bidirectional information exchange between them.

Price-based demand response (PBDR) offers a significant opportunity for electricity consumers to dynamically balance their energy demand in response to time-varying electricity prices, and therefore ease the burden on the grid during peak times. However, despite being the primary energy consumers, there is little research carried out on implementing PBDR in industrial facilities, especially on real-time price (RTP) based DR. In this study, we propose a DR scheme based on hour-ahead RTP for industrial facilities. The scheme implements an artificial neural network based price forecasting model to forecast unknown future prices to support global time horizon optimization. Based on the forecasting price, the energy cost minimization problem is formulated by mixed integer linear programming. This paper includes a practical case study of the whole process of steel powder manufacturing for performance analysis. The results show that the proposed scheme is capable of balancing the energy demand and reducing energy costs while satisfying production targets.

As a major consumer of energy, the industrial sector must assume the responsibility for improving energy efficiency and reducing carbon emissions. However, most existing studies on industrial energy management are suffering from modeling complex industrial processes. To address this issue, a model-free demand response (DR) scheme for industrial facilities was developed. In practical terms, we first formulated the Markov decision process (MDP) for industrial DR, which presents the composition of the state, action, and reward function in detail. Then, we designed an actor-critic-based deep reinforcement learning algorithm to determine the optimal energy management policy, where both the actor (Policy) and the critic (Value function) are implemented by the deep neural network. We then confirmed the validity of our scheme by applying it to a real-world industry. Our algorithm identified an optimal energy consumption schedule, reducing energy costs without compromising production.

The increasing penetration of cyber-physical system (CPS) technologies in industry is transforming this environment into a multifaceted system featuring a tight combination of its physical and computational elements, including their digital (virtual) representations, contributing to the concept of an industrial CPS. The German “Industry 4.0” is a key innovation program aimed at realizing industrial CPS. Industry 4.0 is characterized by digital industrial components interacting with each other to become systems meeting flexible industrial demands, e.g., order-controlled production. An asset administration shell (AAS), as defined in the context of the Reference Architectural Model for Industry 4.0 (RAMI 4.0), is a practical embodiment of the latest buzzword, digital twin, and can be realized with the integration of operation technologies and information and communication technologies. AASs offer an interoperable way to capture key information pertaining to assets, such as intrinsic properties, operational parameters, and technical functionalities, and to enable straightforward interaction over standardized, secure communication with other Industry 4.0 components. The goal of this article is to present the status quo of AAS development, to design an intuitive method for implementing AASs, and to develop an AAS-enabled digital solution for cyber-physical applications in the manufacturing sector. Last but not least, we demonstrate a case study featuring an Industry 4.0 application scenario, i.e., plug-and-produce.