Demand response programs have been receiving more serious attention as alternatives for participating in load frequency control. Inverter air conditioners (IAC) are acknowledged as suitable devices for demand response due to their increasing contribution to network consumption. Despite their potential, their use presents challenges, including delayed responses, variable interference, and the absence of coordination with traditional generation units, which may affect control performance. Also, existing control strategies fail to consider operational and physical constraints, resulting in possible model mismatches. In this paper, a model predictive control with feedback correction (MPCFC) is proposed to dispatch control signals to the IACs so they can effectively participate in the frequency control of an interconnected power system. The feedback correction method is presented to enhance prediction accuracy in the MPC and weaken the influence of model parameter mismatches and external disturbances. Furthermore, to minimize the impacts of communication delays on frequency overshoot/undershoot, this study introduces an intelligent supervisory coordinator based on an artificial neural network to coordinate the reaction of traditional generation units and IACs to correct significant frequency variations brought on by the time delays. The effectiveness of the developed control scheme is verified through numerical studies by comparing it with the IAC with PI and MPC controllers (without coordinator) and the system without IACs. Case studies are investigated on a two-area power system in MATLAB/Simulink environment, and the OPAL-RT real-time simulator is used to validate the results.

This article determines the optimal capacities of small wind turbine (SWT) and battery energy storage (BES) for a grid-connected household (GCH) with or without an electric vehicle (EV) to minimize the overall cost of electricity (COE). Rule-based home energy management systems (HEMSs) are developed for two different configurations of the GCH: 1) with only SWT and 2) with SWT and BES. For each configuration, the HEMSs are developed for two cases: with and without an EV in the premises of the GCH. Uncertainties are also included in the arrival time, departure time, and initial state of charge (at arrival) of the EV. The abovementioned technique is then applied to a typical household in South Australia using the yearly load profile of the household and actual yearly wind speed data at an interval of one hour. To investigate the effects of stochastic nature of household load, EV, and wind power generation on various results, the optimization process is repeated using 10-year of actual wind speed data and probabilistic load and EV uncertainties. The results of several sensitivity analyses of various system parameters are presented. It has been found that the SWT can effectively decrease the COE of the household for both cases (with and without an EV). However, the current price of battery may not be in favor of further reducing the COE of the household.

An automatic detector device (ADD) has been designed and built for monitoring insulators state in overhead distribution lines. The prototyped device is suitable for measurement and analysis of the insulator leakage current (LC) on lines of distribution systems to diagnose insulator state. It operates based on two diagnostic indicators, third to fifth harmonic ratio (R3/5%) of the insulator'sLCsignal and cosine of phase angle difference (P.D%) between fundamental signals of the voltage andLC. Experimental data of the indicators in various conditions and various insulators are used to train a classifier for determining reference values of the indices for insulator state detection. The instrument has two physically separate parts: receiver and transmitter. They communicate via a radio communication channel which improves the effectiveness of incident management. The transmitter samples theLCsignal of the target insulator using a ferrite core along with a series of active electronic filters. The sampledLCsignal is then sent to the receiver. The transmittedLCsignal is analyzed by the microprocessor embedded in receiver CPU. Indicators of the transmittedLCsignal are calculated and compared with reference values extracted from the classifier. The receiver determines the insulator's state based on the comparison of the estimated and reference values of diagnostic indicators for the target insulator. The performance of the prototyped ADD was evaluated experimentally in the laboratory as well as on overhead distribution lines. These performance tests confirmed reliable performance of the ADD.

This article presents a two-stage adaptive robust optimization (ARO) for optimal sizing and operation of residential solar photovoltaic (PV) systems coupled with battery units. Uncertainties of PV generation and load are modeled by user-defined bounded intervals through polyhedral uncertainty sets. The proposed model determines the optimal size of PV-battery system while minimizing operating costs under the worst-case realization of uncertainties. The ARO model is proposed as a trilevel min-max-min optimization problem. The outer min problem characterizes sizing variables as “here-and-now” decisions to be obtained prior to uncertainty realization. The inner max-min problem, however, determines the operation variables in place of “wait-and-see” decisions to be obtained after uncertainty realization. An iterative decomposition methodology is developed by means of the column-and-constraint technique to recast the trilevel problem into a single-level master problem (the outer min problem) and a bilevel subproblem (the inner max-min problem). The duality theory and the Big-M linearization technique are used to transform the bilevel subproblem into a solvable single-level max problem. The immunization of the model against uncertainties is justified by testing the obtained solutions against 36 500 trial uncertainty scenarios in a postevent analysis. The proposed postevent analysis also determines the optimum robustness level of the ARO model to avoid over/under conservative solutions.

In this article, a robust model predictive control (MPC) is proposed for the gate-controlled series capacitor (GCSC) to contribute to load frequency control. The proposed control model consists of a two-layer MPC in which a nominal MPC produces an initial control signal together with a prediction of system frequency response in a nominal system without uncertainty. An ancillary MPC then produces a control command for the actual system with uncertainty based on measurements, and signals provided by the nominal system. The control signals are provided in such a way that the error in the frequency response of the actual system is minimized with respect to that of the nominal system. Optimization procedures are also conducted to attain optimal values of weighting coefficients associated with the input, and output in the objective functions. Constraints associated with system, and GCSC control are also taken in providing the control signals into consideration. A linear model is presented to formulate the contribution of GCSC control to the power flow of tie-line. The effectiveness of the proposed method in dealing with uncertainties is compared with a conventional MPC, a scheme with proportional-integral controllers, and a system without GCSC. The method robustness is also illustrated by case studies with load uncertainty, and wind power fluctuations, and also uncertainty of parameters. The impact of the delay in communication links on the performance of the control scheme is also evaluated.

Abstract The link prediction problem is becoming an important area of online social network (OSN) research. The existing methods that have been developed to address this problem mostly try to predict links based on structural information about the whole of the user lifespan. In addition, most of them do not consider user attributes such as user weight, density of interaction and geo-distance, all of which have an influence on the prediction of future links in OSNs due to the human-centric nature of these networks. Moreover, an OSN is a dynamic environment because users join and leave communities based on their interests over time. Therefore, it is necessary to predict links in real time. Therefore, the current study proposes a new method based on time and user attributes to predict links based on changes in user communities, where the changes in the user communities are indicative of users’ interests. The proposed method is tested on the UKM dataset and its performance is compared with that of 10 well-known methods and another community-based method. The area-under-the-curve results show that the proposed method is more accurate than all of the compared methods.

The distance between users has an effect on the formation of social network ties, but it is not the only or even the main factor. Knowing all the features that influence such ties is very important for many related domains such as location-based recommender systems and community and event detection systems for online social networks (OSNs). In recent years, researchers have analyzed the role of user geo-location in OSNs. Researchers have also attempted to determine the probability of friendships being established based on distance, where friendship is not only a function of distance. However, some important features of OSNs remain unknown. In order to comprehensively understand the OSN phenomenon, we also need to analyze users’ attributes. Basically, an OSN functions according to four main user properties: user geo-location, user weight, number of user interactions, and user lifespan. The research presented here sought to determine whether the user mobility pattern can be used to predict users’ interaction behavior. It also investigated whether, in addition to distance, the number of friends (known as user weight) interferes in social network tie formation. To this end, we analyzed the above-stated features in three large-scale OSNs. We found that regardless of a high degree freedom in user mobility, the fraction of the number of outside activities over the inside activity is a significant fraction that helps us to address the user interaction behavior. To the best of our knowledge, research has not been conducted elsewhere on this issue. We also present a high-resolution formula in order to improve the friendship probability function.

In this paper, we have proposed a dual-channel supply chain model in uncertain environment to analyze the demand of manufacturer and retailer demand in which the profit being maximized. Linguistic terms are also utilized to establish two fuzzy systems for estimating the demand in direct and retail channels. In order to do that, a mathematical model is proposed based on decentralized situation of supply chain. To solve the model, we have developed a hybrid solution method of genetic algorithm, fuzzy system, and L-P metric. Finally, several test problems are first generated; then, the computational results are analyzed.