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.

Recently, time-sensitive networking (TSN) has been developed to incorporate real-time capabilities to the standard Ethernet for the incompatible issue in existing industrial communication solutions and is considered a promising solution to satisfy the requirements of the Industrial Internet of Things (IIoT). However, the routing and scheduling methods are left out of the TSN standard. This study developed a novel joint traffic routing and scheduling algorithm to compute routes for time-critical flows and construct the schedules. The validity of the proposed algorithm was verified using a random synthetic test scenario. The simulation results demonstrated that the real-time requirements of time-critical traffic could be satisfied with the proposed algorithm. Subsequently, we evaluated the scalability of the proposed algorithm from the perspective of the number of flows that can be scheduled and the network size. The results showed that the computational times for 4000 flows in the realistic industrial network topology and random network topologies (with up to 21 switches and 105 end stations) are at the subsecond level, which indicated the perfect scalability of the proposed algorithm. In addition, compared with the well-known integer linear programming (ILP)-based approach, Degree of Conflict (DoC)-aware iterative routing and scheduling (DA/IRS) approach, and hybrid genetic algorithm (HGA)-based approach, the proposed algorithm is faster than the three approaches by at least an order of magnitude.

Industrial communication requires highly reliable networks with hard temporal constraints. Thus, the IEEE 802.1 Task Group proposed a promising technology, namely, time-sensitive networking (TSN), to complement the determinism and real-time (RT) capabilities of Ethernet via a set of enhanced IEEE standards. To explore the feasibility and applicability of TSN, we developed a simulation model for TSN using a module-based design method. Our TSN-compliant modules implement time-based traffic scheduling functionality to guarantee the deterministic and RT transmission of time-triggered traffic. We validated the developed modules by comparing the end-to-end simulation latencies to those measured in a real-world TSN testbed. The results of this evaluation validate the proposed simulation model and show that it conforms tightly to the TSN specifications and faithfully matches the real-world testbed.

This paper presents a novel incentivizing strategy for a demand response (DR) aggregator to participate in an intra-day market organized by the grid operator (GO). By taking the market entry criterion into consideration, the proposed incentivizing strategy is able to help the aggregator determine the optimal incentive for each customer based on their identified characteristics or preferences. The hierarchical decision making procedure between the aggregator and customers is casted into a Stackelberg game. In accordance, an optimal incentivizing strategy can be ascertained for the aggregator, which further helps the aggregator elicit optimal load reductions from enrolled customers meanwhile maximizing its potential profit. Simulation results show that the proposed incentivizing strategy is effective in helping the aggregator determine optimal incentive for each customer in face of different external factors.

The limited spectrum resources inevitably incur the spectrum sharing among coexisting industrial wireless networks (IWNs), and multiple coexistence IWNs form a heterogeneous environment. An effective resource allocation thus plays a crucial role in coordinating the efficient operations of multiple IWNs. Existing works only study the constrained coexistence problem among specified types of networks with a limited number of nodes over one single channel. In this paper, we investigate a general coexistence problem over multiple channels among arbitrary types of networks with line topologies, and the number of nodes in each network is also arbitrary. We rigorously analyze theoretical scheduling latency of this general coexistence problem, then we propose an algorithm to attain the optimal result. The presented Coexisting Line topology Networks Resource Allocation (CLNRA) algorithm consists of two phases. In the inter-network resource allocation phase, non-overlapped channels are allocated to each network according to the corresponding transmission priority. While in the intra-network resource allocation phase, we filter out the nodes that may generate continuous empty buffers so as to enhance the resource utilization ratio. We also verify the effectiveness of the CLNRA algorithm through extensive simulations. Evaluation results show that the CLNRA algorithm can attain the theoretical optimal result in 99:3% cases, and it has obvious superiorities on resource utilization ratio and scheduling latency.

Diverse customer demands are increasing the need for improved flexibility, adaptability, and transparency of production processes. This poses new challenges to modern manufacturing systems. Fortunately, advances associated with the fourth industrial revolution have provided many solutions to these challenges. In this study, an AutomationML (AML) and OPC UA-based solution is proposed for manufacturing process. By integrating AML and OPC UA, we developed a four-layer architecture dedicated to a manufacturing system referencing the Reference Architecture Model for Industry 4.0 (RAMI 4.0). Additionally, an experimental system was constructed to demonstrate the availability and applicability of the proposed approach. The system execution processes was also elaborated to better understand the processing of information flows across the whole system.

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.

Demand response (DR) smart grid technology provides an opportunity for electricity consumers to actively participate in the management of power systems. Industry is one of the major consumers of electric power. In this study, we propose a DR energy management scheme for industrial facilities based on the state task network (STN) and mixed integer linear programming (MILP). The scheme divides the processing tasks in industrial facilities into nonschedulable tasks (NSTs) and schedulable tasks (STs), and takes advantage of distributed energy resources (DERs) to implement DR. Based on day-ahead hourly electricity prices, the scheme determines the scheduling of STs and DERs in order to shift the demand from peak periods (with high electricity prices) to off-peak periods (with low electricity prices), which not only improves the reliability of the electric power system, but also reduces energy costs for industrial facilities.

Communication networks are used to effectively manage and control building facilities. Using a wireless channel in building automation systems would result in several advantages, including reduced installation cost and eased placement constraints on the transceivers, as well as the introduction of flexible extensions and mobile device connectivity. Recently, ZigBee was adopted as a BACnet wireless channel by both BACnet and ZigBee organizations. This paper proposes an efficient energy management scheme for the lighting system of buildings using the BACnet-ZigBee gateway and ZigBee CBA profile-based wireless sensor, controller and actuator nodes. The proposed energy management schemes were examined using an experimental facility of a distributed lighting control system. This study can be extended to the development of a BACnet-ZigBee-based smart grid system in buildings.

The use of wireless sensor networks in home automation (WSNHA) is a growing trend, on account of their self-organisation, low cost and potential for rapid deployment. Routing in WSNHA is a challenging research topic, because of the requirements of low energy consumption, low storage, low computational complexity, fast response, self-adaptation to unpredictable topological changes and robustness with respect to discontinuous interference. With the development of localisation techniques, localised routing offers a number of attractive benefits, but not all are suitable for WSNHA. In this study, the authors propose a new routing algorithm for WSNHA, called WSNHA-GAHR (greedy and A* heuristic routing for WSNHA). The algorithm uses greedy forwarding, based on the smallest Euclidean distance, to minimise the number of hops for data packet transmission, and thus conserve energy. It also automatically adjusts the route, using the A* heuristic algorithm, when local minimum problems and unpredictable topological changes occur in WSNHA. In addition, link priority classification gives WSNHA-GAHR a greater tolerance than ZigBee routing algorithm for discontinuous interference. Simulation results show that this routing protocol dramatically improves reliability and reduces average packet delay, without introducing additional overhead.

This paper presents an approximate analysis of delay performance in the timer-controlled priority scheme of single-service token passing systems, where each priority queue is allowed to transmit one message at a time. This is the practical case of real-time networks such as those used in aircraft and spacecraft control, process control and factory communications. The token passing systems consist of N stations, each of which has (K+1) priority queues. The highest priority queues can transmit a message whenever the token arrives. A lower priority queue can transmit a message if the token rotation time (which was reset and restarted at the previous token arrival instant) is not expired when the token arrives. An analytical model is developed based on the independent and identically distributed random variables of conditional effective service times (time interval between two consecutive instants at which a priority queue has an opportunity to transmit a message) of each priority queue. The condition is assigned on the basis of the contribution of message transmission time to the effective service time. The approximate mean waiting time for each priority queue is determined using the well-known results from Kuehn (1979), who first introduced the conditional cycle time. The approximate analytical model is validated by comparison with the simulation results.>