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.

The fourth industrial revolution, also termed Industry 4.0 (I4.0), will digitalize manufacturing operations and decentralize them from the shop floor to the office and across entire enterprise networks [1]. The proposed system-level digitalization solutions for I4.0 include cyber-physical systems [2] and digital twins [3]. Standardized I4.0 technologies are urgently required to change industry and guide market usage. The asset administration shell (AAS) of the I4.0 component [4] is a German symbolic model that is under development as an international standard in International Electrotechnical Commission (IEC) project 63278-1. Not all engineers, researchers, and entrepreneurs have a basic knowledge of the AAS. Very few understand how to employ an AAS in practice. This article uses AASs to implement an I4.0 scenario, thus plug and produce (PnP) of industrial field devices. A three-layer, AAS-based implementation of PnP manufacture in line with I4.0 is developed. The synergy is to show that the AAS may help expedite I4.0 digitalization and propel its applications forward.

The value of demand response (DR) for improving the operational efficiency of smart grids has been widely recognized. To further explore the potential DR flexibility, this article presents a novel framework for DR resource trading between multiple service providers (SPs) and end customers, in which SPs are supposed to offer diversified incentives to a resource trading center (RTC) while anticipating responses from the demand side. Distinguished from previous studies, in this work each end customer is endowed the authority to simultaneously trade with multiple SPs with the intention of maximizing their net utilities. The trading process between SPs and customers is demonstrated by a multileader multifollower Stackelberg game, due to its superiority of modeling problems with hierarchical decision-making behaviors. To solve the formulated game, an exact potential function is proposed to help determine the unique Nash equilibrium (NE) among SPs, which serves as the cornerstone for further deriving the Stackelberg equilibrium (SE), i.e., the ultimate outcome of the resource trading process. Simulation results verified that the DR flexibility was significantly increased compared with the case when there is only one SP responsible for the entire trading on behalf of all the end customers. Moreover, the proposed approach is also able to bring extra benefits for both SPs and customers in terms of net profit and net utility value.

The multifaceted industrial application of cyber-physical systems (CPSs) has increased, enabling tight interactions between physical and computational elements. Industry 4.0 aims to promote industrial application of CPSs. The asset administration shell (AAS) represents the practical embodiment of the CPS, and can be obtained by integrating information and communication technologies. Most prior studies concentrated on AASs for hardware assets in the manufacturing domain; they seldom considered AASs for software applications. To fill this gap, we present a method for implementing AASs for software, i.e., a manufacturing execution system (MES) combined with an enterprise resource planning (ERP) system. This method allows runtime data exchange between an MES and ERP; this is better than the conventional file-based method. Finally, a data exchange case (a manufacturing order) is presented to validate our AAS method.

Demand response (DR) has been widely recognized as an effective solution to help mitigate the stresses imposed on power grids. As new concepts evolve, DR induces various interactions among multiple emerging entities, which further complicates the decision-making processes in grid operations. Recently, game theory (GT) has received great attention in DR management, due to its ability to handle complex decision-making problems. Numerous theoretical GT-based approaches have been proposed for addressing various DR issues, but the feasibility of these theoretical approaches in practical implementation remains in doubt. To bridge the gap between theoretical studies and practical implementations, we first provide specific guidelines regarding how to construct a DR-oriented facility, and then investigate the effectiveness of deploying a Stackelberg game theory-based DR algorithm to manage the energy consumption of the facility, wherein the energy management center (EMC) serves as the leader and multiple devices act as the followers. The experimental evaluation results show that the GT-based DR algorithm achieved great performance in practical DR management, including optimal load control in responding to real-time price (RTP), and peak load reduction with a peak-to-average ratio (PAR) of 1.59.