In this correspondence, we analyze the effects of noisy links on the steady-state performance of diffusion least-mean-square (LMS) adaptive networks. Using the established weighted spatial-temporal energy conservation argument, we derive a variance relation which contains moments that represent the effects of noisy links. We evaluate these moments and derive closed-form expressions for the mean-square deviation (MSD), excess mean-square error (EMSE) and mean-square error (MSE) to explain the steady-state performance at each individual node. The derived expressions, supported by simulations, reveal that unlike the ideal link case, the steady-state MSD, EMSE, and MSE curves are not monotonically increasing functions of the step-size parameter when links are noisy. Moreover, the diffusion LMS adaptive network does not diverge due to noisy links.

This paper presents a game theoretic decentralized electric vehicle charging schedule for minimizing the customers' payments, maximizing the grid efficiency, and providing maximum potential capacity for ancillary services. Most of the available methods for electric vehicle charging assume that the customers are rational, there is low-latency perfect two-way communication infrastructure without communication/computation limitation between the distribution company and all the customers, and they have perfect knowledge about the system parameters. To avoid these strong assumptions and preserve the customers' privacy, we take advantages of the regret matching and the Nash Folk theorems. In the considered game, the players (customers) interact and communicate locally with only their neighbors. We propose a mechanism for this game which results in a full Nash Folk theorem. We demonstrate and prove that the on-off charging strategy provides maximum regulation capacity. However, our mechanism is quite general, takes into account the battery characteristics and degradation costs of the vehicles, provides a real time dynamic pricing model, and supports the vehicle-to-grid (V2G) and modulated charging protocols. Moreover, the developed mechanism is robust to the data disruptions and takes into account the long/short term uncertainties.

Many problems in multiagent networks can be solved through distributed learning (state estimation) of linear dynamical systems. In this paper, we develop a partial-diffusion Kalman filtering (PDKF) algorithm, as a fully distributed solution for state estimation in the multiagent networks with limited communication resources. In the PDKF algorithm, every agent (node) is allowed to share only a subset of its intermediate estimate vectors with its neighbors at each iteration, reducing the amount of internode communications. We analyze the stability of the PDKF algorithm and show that the algorithm is stable and convergent in both mean and mean-square senses. We also derive a closed-form expression for the steady-state mean-square deviation criterion. Furthermore, we show theoretically and by numerical examples that the PDKF algorithm provides a trade-off between the estimation performance and the communication cost that is extremely profitable.

In this paper, we consider the demand response problem in smart grid consisting of a retailer and multiple residential consumers, where the retailer determines consumers’ payments based on their power consumption profile. Our aim is to propose a fully distributed algorithm that is able to optimize the aggregate cost, utility, and retailer's profit simultaneously. To this end, we first formulate the consumer-side trend as a constrained convex optimization problem and propose a fully distributed adaptive diffusion algorithm to solve it. In addition, we design a one-leader $N$-follower Stackelberg game to model interactions among the retailer and consumers. The proposed framework is able to continuously track the drifts resulting from the changes in the real-time pricing or the consumer preferences. Moreover, it is scalable and does not require network-wide information or rely on central controller. We provide comprehensive simulation results to show the effectiveness of the proposed framework.

In this study, an improved artificial intelligence algorithms augmented Internet of Things (IoT)-based maximum power point tracking (MPPT) for photovoltaic (PV) system has been proposed. This will facilitate preventive maintenance, fault detection, and historical analysis of the plant in addition to real-time monitoring. Further, the simulation results validate the improved performance of the suggested method. To demonstrate the superiority of the proposed MPPT algorithm over current methods, such as cuckoo search algorithms and the incremental conductance approach, a performance comparison is offered. The outcomes demonstrate the suggested algorithm’s capability to track the Global Maximum Power Point (GMPP) with quicker convergence and less power oscillations than before. The results clearly show that the artificial intelligence algorithm-based MPPT is capable of tracking the GMPP with an average efficiency of 88%, and an average tracking time of 0.029 s, proving both its viability and effectiveness.