The smart energy management system as a powerful tool is implemented to manage both demands and generation units. The energy management problem in a Microgrid is usually formulated as a nonlinear optimization problem. According to nonlinear and discreet nature of the problem, solving it by a centralized method requires high computational capabilities. In this paper, two DISTRIBUTED energy management system called Alternating Direction Method of multiplier Predictor (ADMM) and Corrector Proximal Multiplier (PCPM) have been investigated in order to jointly schedule the central controller as well as local controllers. The ALGORITHMs consider optimal power flow equations within the DISTRIBUTED energy management problem. The proposed DISTRIBUTED ALGORITHMs have been investigated on a typical MG and the efficiency of the ALGORITHM has been evidenced through case studies. Simulation results show that the proposed method decreases the operational cost of MG. Also, the results evidenced that the ADMM has been converged faster and provided a lower operation cost if compared to the PCPM.

Recently, DISTRIBUTED Constraint Optimization Problems (DCOP) have been drawing a growing body of attention as an important research area in multi agent systems as a large body of real problems can be modeled by them. The primary goal of this research is to design a DISTRIBUTED and effective ALGORITHM to solve DCOP. There are various criteria that measure the efficiency of DCOP ALGORITHMs, but the most efficient ALGORITHM for DCOP is the one by which the computation and communication cost is as low as possible and the quality of the solution is high. In this paper, we focus on an approximate DCOP ALGORITHM called DALO (DISTRIBUTED Asynchronous Local Optimization). Using the main idea of the DALO ALGORITHM, we propose a new ALGORITHM to solve DCOP, which exhibits two important improvements over the DALO ALGORITHM. First we use a sequential partial approach to select a coefficient of leaders to compute the best assignment for agents by which the computation and communication cost decrease in the whole DCOP. The second improvement is an evolutionary approach by which the computation and communication burden for each agent decreases. We present some empirical evidences that show our ALGORITHM performs better than the DALO ALGORITHM.

The emerging field of compressive sensing enables the reconstruction of the signal from a small set of linear projections. Traditional compressive sensing approaches deal with a single signal; while one can jointly reconstruct multiple signals via DISTRIBUTED compressive sensing ALGORITHM, which exploits both the inter-and intra-signal correlations via joint sparsity models. Since the wavelet coefficients of many signals is sparse, in this paper, the wavelet transform is used as sparsifying transform, and a new wavelet-based Bayesian DISTRIBUTED compressive sensing ALGORITHM is proposed, which takes into account the inter-scale dependencies among the wavelet coefficients via hidden Markov tree model, as well as the inter-signal correlations. This paper uses Bayesian procedure to statistically model this correlation via the prior distributions. Also, in this work, a type-1 joint sparsity model is used for jointly sparse signals, in which every sparse coefficient vector is considered as the sum of a common component and an innovation component. In order to jointly reconstruct multiple sparse signals, the centralized approach is used in DISTRIBUTED compressive sensing, in which all the data is processed in the fusion center. Also, variational Bayes procedure is used to infer the posterior distributions of unknown variables. Simulation results demonstrate that the structure exploited within the wavelet coefficients provides superior performance in terms of average reconstruction error and structural similarity index.

This paper presents a new approach for radial DISTRIBUTED network planning with using ACO ALGORITHM. Ant colony optimization ALGORITHM (ACO) is one of the best methods for optimization in difficult discrete problems. The new ALGORITHM determines the optimal configuration of network and voltage level of each section of the feeders and calculates the optimum rating of the transformers and the size of each section of the feeders. This ALGORITHM applied to a real 23-feeder radial test network of Gilan Regional Electricity Company in Iran. The results validate the superiority of new ALGORITHM in comparison with particle swarm optimization (PSO) and supplying area ALGORITHM. The important characteristics of this ALGORITHM in this paper are its high speed in solving problems for instance 240 times of supplying area ALGORITHM time and 18 times of PSO ALGORITHM time and 10% decreasing of final cost because of considering the transformers rating as an another searching space variable.

Robust adaptive estimation of unknown parameter has been an important issue in recent years for reliable operation in the DISTRIBUTED networks. The conventional adaptive estimation ALGORITHMs that rely on mean square error (MSE) criterion exhibit good performance in the presence of Gaussian noise, but their performance drastically decreases under impulsive noise. In this paper, we propose a robust adaptive estimation ALGORITHM for networks with cyclic cooperation. We model the impulsive noise as the realization of alpha-stable distribution. Here, we move beyond MSE criterion and define the estimation problem in terms of a modified cost function which exploits higher order moments of the error. To derive a DISTRIBUTED and adaptive solution, we first recast the problem as an equivalent form amenable to DISTRIBUTED implementation. Then, we resort to the steepest-descent and statistical approximation to obtain the proposed ALGORITHM. We present some simulations results which reveal the superior performance of the proposed ALGORITHM than the incremental least mean square (ILMS) ALGORITHM in impulsive noise environments.