Cyber-Physical Distributed Systems. Min Xie
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among network interface cards across communication channels. Several MAC protocols, for example, carrier‐sense multiple access with collision detection (CSMA/CD, Ethernet), CSMA with arbitration on message priority (controller area network), and IEEE 802.11b/g (WLAN), prevent the collision of packets sent from different nodes across the same channel [83,93–95].
Time delays are variable, challenging to predict, deteriorate AGC performance, and reduce the stability region [78,79]. Packet dropouts refer to lost messages, which occupy network bandwidth but cannot reach the destination. They affect the operations of DERs and the reduction of frequency fluctuations, particularly in uncertain network environments. Optimal feedback AGC regulators for DERs have been investigated in numerous works for perfect communication networks, and the impact of transmission delays and packet dropouts on the controller cannot be captured [96]. Robust PID controllers against constant or uniformly distributed time delays [77–80] are designed to cope with perturbations of the control parameters. However, constant or uniformly distributed time delays cannot be generally assumed in realistic communication networks.
In addition, recent studies focusing on primary and secondary control levels have been extended to the power management level by considering fuzzy controllers [97,98], decentralized power management and sliding mode control strategies [99], static synchronous compensators [100], and two‐degrees‐of‐freedom feedback‐feedforward robust controllers [101,102]. The reactive power reference can be determined and controlled by a novel application of radial basis function neural networks [103–105] to improve the power sharing and stability of microgrids with multi‐DERs. To provide high reliability and robustness against network failure or time delays, droop‐based control schemes are designed to specify the frequency of each DER unit by using complementary loops and fuzzy logic controllers [107], robust H∞ controllers [86], and PI controllers [107,108]. On the other hand, novel approaches for mitigating the impact of random time delays quantify robust delay margins [109]. The delay margin‐based sparsity‐promoting wide‐area control strategy, which requires few system observations, can reduce communication requirements and yield nearly optimal performance compared with centralized control [109]. Nevertheless, packet dropout still has the potential to affect the performance of this strategy.
1.2.2 Reliability of CPSs
Distributed renewable energy sources are increasingly connected to power distribution networks as a remedy for environmental and economic concerns [110–112]. However, their power outputs are dependent on the available intermittent natural resources, such as solar irradiation, wind velocity, and biofuel production [113–115]. The rapid deployment and commercialization of storage devices and electric vehicles (EVs) has become an attractive technological solution to facilitate the use of renewable energy sources, manage demand loads, and decarbonize the residential sector [115–117]. The above technological issues call for managing real‐time energy imbalance in DGSs to meet electricity demand over a long‐term horizon. In order to address the challenges of distributed control of energy sources, communication networks are being installed for accurate control of the different power sources and the timely operational scheduling of distributed generator (DG) units, with the objective of providing reliable and sustainable energy in a timely fashion [118–123]. However, most existing research works do not formally investigate the capability of communication networks in providing real‐time power management and promoting the optimal power dispatch [124–127]. The effective integration of communication networks into DG systems is a key step in the realization of future smart grids [90,128].
Most integrated system‐of‐systems models have been developed based on dedicated and closed communication networks, where the infrastructure is exclusively built for smart grid applications [90,128,129]. As the network is dedicated between the DG and the control center, the data exchange is assumed to be perfect and free of defects (e.g., induced time delays and packet dropouts [130–134]). However, experience has shown that dedicated communication networks are ill‐suited to future DG systems, which require a different, more complex but much cheaper network, as its dimension would be much larger [135–137]. Because of the low installation cost, high transmission speed, and flexible access, the open communication network has the highest potential for integration with future DGSs [82,84,138]. As end‐users have to share the limited bandwidth in the open communication networks, which could lead to local congestion, they can be unreliable and suffer from network‐induced delays and packet dropouts [139–141].
Existing research works [42,123,139–146] do not model explicitly and adequately the behaviors of transmission delays and packet dropouts. Most of the aforementioned models are limited to constant or less stochastic transmission delays, which are not true in reality [147,148]. The delays are described by discrete‐time models or are neglected by assuming that they are much smaller than the communication interval [17,123,149,150]. Packet dropouts are usually modeled by a two‐state Markov chain and the associated quantitative loss rates; the detailed state evolution is masked and only input/output information is made available. The state transition matrix is known by assuming that the evolutions of packet dropouts can be fully observed [123,140]. Additionally, the models of uncertain renewable power sources do not consider time‐correlated properties [42,123,139,142,143,151]. Consequently, the control schemes derived based on such assumptions can be very conservative and may not be readily applicable to real systems.
To bridge this gap, a generic and transparent mathematical model is necessary for the analysis of the impacts of integration of unreliable open communication networks (e.g., home area networks, neighborhood area networks, and WANs). The major challenges lie in the modeling and simulation of the interactions between degraded communication networks and DG systems, and the optimization of the real‐time energy management problem on such system platforms. Note that the specific requirements (i.e., high‐frequency data and data prediction) introduced by communication network integration need to be taken into account in this modeling, simulation, and optimization framework.
1.3 Opportunities for CPS Applications
1.3.1 Managing Reliability and Feasibility of CPSs
CPSs perform critical tasks in many industrial applications, for example, manufacturing systems [152], transportation systems [153,154], and power systems [18,155]. The components of control systems, that is, actuators and sensors, are subject to degradation when operating under severe working conditions [155–159].
Sudden load variations in electric power systems are often balanced by promptly changing the output of natural gas power plants following the LFC strategy [160]. However, the degradation of gas turbine compressors, that is, the deviation of compressor flow capacity and isentropic efficiency [161], and the degradation of PMUs, that is, measurement drifts and errors [162,163], reduce the LFC performance by decreasing the available balancing power, and by producing inaccurate frequency readings. Deteriorated LFC performance may result in power system failures because the system frequency exceeds its maximum allowable drop or fails to attain the steady‐state frequency tolerance band in the required time in compliance with ISO 8528‐5 [164]. As a result, power system failures are determined by the LFC performance, stemming from the partial information on the power system conditions, that is, the health indexes of gas turbines (flow capacity and isentropic efficiency), and measurement drift. Therefore, it is necessary to study these degradation processes to predict the real‐time LFC performance loss and ensure adequate LFC through proper maintenance activities.
Lifetime prognostic studies of gas turbines [165] have shown that the failure time of these systems varies between 24 000 h and 35 000 h. Such variability stems from different working conditions [166] and different starting points of the degradation paths [167]. Therefore, the degradation model should reflect the unit‐to‐unit variability in the degradation process of gas turbines [168]. Moreover, many condition‐based maintenance (CBM) models determine maintenance activities based on the estimated remaining useful lifetime (RUL) of gas turbines using health indexes (reduction in flow capacity and isentropic efficiency [161]) derived from on‐line measurements, for example, rotor speed, inlet temperature, and pressure [169,170]. Maintenance activities are scheduled