Abstract
The penetration level of plug-in electric vehicles (PEVs) has a potential to be remarkably increased in the near future. As a result, the smart power systems will have new challenges and opportunities. The energy storing capability of PEVs is an attractive capability that enables PEVs to participate in providing ancillary services, e.g., load frequency control (LFC). This paper evaluates the participation of PEVs in load frequency control of a microgrid (MG) by using a decentralized multi-agent based control system. According to the proposed multi-agent based scheme, each PEV is considered as an agent that makes a synchronized decision for the participation in the LFC according to the global information. The required global information is discovered through the average consensus algorithm (ACA). The effect of time delay on the proposed method is investigated. Simulation studies are carried out in MATLAB-Simulink and show the effectiveness of the proposed decentralized control scheme.

Abstract
This paper simulates an islanding network including wind turbine, battery energy storage systems (BESS), and load. The purpose is to control voltage and frequency of the load following wind speed variations by proper control of BESS. A decentralized control scheme including two control loops is designed on BESS. One control loop is implemented for voltage regulation and the other loop is designed for frequency control. Both loops are equipped with PI (Proportional-Integral) type controllers as internal controllers. Furthermore, both loops are equipped with supplementary stabilizers as external controllers. The internal controllers regulate frequency and voltage and the external stabilizers enhance stability. This paper optimally tunes all the parameters of internal controllers and external stabilizers at the same time. The problem for tuning a large number of the design variables is mathematically expressed as a mixed integer nonlinear optimization programming and solved by modified-adaptive PSO technique. The proposed methodology is simulated on a typical standalone network including wind turbine, BESS, and load. The accurate model of BESS and wind turbine is incorporated to cope with real conditions. Moreover, in order to demonstrate the real-world results, non-linear time domain simulations are carried out in MATLAB software. The results verify that the proposed control scheme can efficiently utilize BESS to control voltage, regulate frequency, and damp out oscillations under wind and load variations.

Abstract
This paper proposes a framework for the optimal operation of multi Micro Grids (multiMGs) based on Hybrid Stochastic/Robust optimization. MultiMGs with various characteristics are considered in this study. They are connected to different buses of their Up-Stream-Network (USN). Day-Ahead (DA) and Real-Time (RT) markets are contemplated. The proposed optimization structure in this paper is a bi-level one since both MGs operators' and USN operator's decisions are considered in the proposed model. The advantages of using time-of-use demand response programs on the optimal operation of USN in the presence of multiMGs are investigated. The uncertainty of different components, including wind units, photovoltaic units, plug-in electric vehicles, and DA market price is captured by using stochastic programming. In addition, robust programming is utilized for contemplating the uncertainty of the RT market price. Furthermore, the grid-connected and island modes of MGs' operation are investigated in this paper, discussing also the virtues of utilizing multiMGs over single MG. Finally, IEEE 18-bus and 30-bus test systems are considered for MGs and USN networks respectively to scrutinize the simulation results.

Abstract
Short-term load and price forecasting is an important issue in the optimal operation of restructured electric utilities. This paper presents a new intelligent hybrid three-stage model for simultaneous load and price forecasting. The proposed algorithm uses wavelet and Kalman machines for the first stage load and price forecasting. Each of the load and price data is decomposed into different frequency components, and Kalman machine is used to forecast each frequency components of load and price data. Then a Kohonen Self Organizing Map (SOM) finds similar days of load frequency components and feeds them into the second stage forecasting machine. In addition, mutual information based feature selection is used to find the relevant price data and rank them based on their relevance. The second stage uses Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for forecasting of load and price frequency components, respectively. The third stage machine uses the second stage outputs and feeds them into its MLP-ANN and ANFIS machines to improve the load and price forecasting accuracy. The proposed three-stage algorithm is applied to Nordpool and mainland Spain power markets. The obtained results are compared with the recent load and price forecast algorithms, and showed that the three-stage algorithm presents a better performance for day-ahead electricity market load and price forecasting.

Abstract
This paper presents a control method for modular multilevel converters (MMCs) as an interface between renewable energy sources and the grid. With growing penetration of renewable energy sources in the power grid, the developments in converter technologies and controller designs become more prominent. In this regard, dynamic and steady state analysis of the proposed model for an MMC use in a renewable energy based power system are provided through dc, 1st, and 2nd harmonic models of the converter in dq reference frame. This detailed configuration is then used to accomplish converter modulation and controller design. The first novel contribution of this control method is to provide an accurate pulse width modulation (PWM) strategy based on network and converter parameters, in order to achieve a stable operation for the interfaced MMC during connection of renewable energy sources into the power grid. In addition, the proposed method is able to mitigate the converter circulating current by inserting a second harmonic reference in the modulation process of the MMC, which is the second contribution this paper provides over other control techniques. A capacitor voltage balancing algorithm is also included in this control method to adjust each sub-module (SM) voltage within an acceptable range. Finally, converter's maximum stable operation range is determined based on the dynamic equations of the proposed model. The functionality of the proposed control method is demonstrated by detailed mathematical analysis and comprehensive simulations with MATLAB/Simulink.

Abstract
The uncertainty and variability of renewable energy sources, wind energy in particular, poses serious challenges for the optimal operation and planning of power systems. In this paper, in order to obtain flexible market conditions while power generated by renewable units is short and supply and demand are imbalanced, a Demand Response (DR) strategy is studied to provide network requirements, because Demand Response Programs (DRPs) improve demand potential and increase security, stability and economic performance. The proposed hybrid model created by the integration of wind energy and DR using Time of Use (ToU) or Emergency DRP (EDRP) improves supply and demand. The problem is solved considering the Independent System Operator (ISO) and using a stochastic multiple-objective (MO) method. The objective is to simultaneously minimize the operation costs and the environmental pollution while assuring compliance of network security constraints and considering multiple economical and technical indexes.