Abstract
In this paper, the local control structure of amicrogrid is partiallymodified by a Lyapunov-based controller. This controller is derived based on direct Lyapunov stability theory (DLST) in order to calculate proper switching functions for the stable operation of the local controller as well as proper local performance of each inverter-based distributed generation (DG) unit. The main contribution is the use of DLST-based controller in a hierarchical primary control structure along with a DC-side voltage regulator. A current-based droop controller is also introduced along with a voltage harmonic compensation technique. The control limits of droop equations are calculated based on steady-state and dynamic capability curve as well as voltage-frequency ellipse curve. The effect of the variations of voltages and circuit parameters on the capability curves are also investigated and the microgrid (MG) steady-state operation area is obtained. In the proposed method, the DC-voltage variations are regulated by an additional voltage control loop based on a current reference correction signal. The above-mentioned approaches are derived thoroughly with mathematical equations. The effectiveness of the designed controllers is verified by a MATLAB/SIMULINK simulation platform (Matlab/Simulink R2014a, Mathworks, Inc.) with harmonically distorted intermittent loads. The results show the appropriate performance of the proposed controllers during both steady-state and transient dynamic conditions.

Abstract
The aim of this paper is to avoid overloading a private customer distribution transformer (DT) in a Portuguese insular area through the means of a new smart electric vehicle (EV) charging scheduler. Firstly, the consequence of the penetration of EVs on the dielectric oil deterioration of the DT of the industrial unit is estimated. The workplace allows the EVs to charge while their owners are working at three different working shifts during a day. Secondly, the EV charging scheduler is tested and the result scenarios are analyzed. This paper shows that the scheduling solution enables the industrial unit to avoid overloading the DT. It also allows to reduce the loss-of-life (LoL) of the DT, while recharging all EVs connected at the beginning of each working shift. © 2016 IEEE.

Abstract
Considering the limitations of traditional energy-saving policies, a kind of energy conservation method called the Information Feedback to Residential Electricity Load Customers, which could impact the demand response capacity, has increasingly received more attention. However, most of the current feedback programs provide the same feedback information to all customers regardless of their diverse characteristics, which may reduce the energy-saving effects or even backfire. This paper attempts to investigate how different types of customers may change their behaviors under a set of customized feedback. We conducted a field survey study in Qinhuangdao (QHD), China. First, we conducted semi-structured interviews to classify four groups of customers of different energy-saving awareness, energy-saving potential, and behavioral variability. Then, 156 QHD households were surveyed using scenarios to collect feedback of different scenarios. Social science theories were used to guide the discussion on the behavior changes as a result of different feedback strategies and reveal the reasons for customers' behaviors. Using the Chi-Square test of independence, the variables that have strong correlations with the categories of residents are extracted to provide references for residents' classification. Finally, the practical implications and needs for future research are discussed.

Abstract
Overtime, in the electricity sector, there has been a technological transfer to renewable electricity generation. With this change, processes, in the economic and availability terms, are expected to improve. In this new paradigm, society demands electricity without an impact on the environment and with the lowest possible cost. The wind power (WP) integration appears in this evolution process by achieving important technological advances, supporting in 2017 a growth of 44% of new projects in Europe, higher than any other renewable technology. However, the renewable energy sources (RES) integration in the electricity networks still presents technical difficulties and challenges, leading to challenges in the electricity markets (EMs). Therefore, this work evaluates the importance of WP and its influence on the Iberian Electricity Market (MIBEL), at the level of the intraday electricity spot market (IESM). This is an innovative study because literature usually focuses on day-ahead WP impact and this study focuses on intraday markets, which are closer to the consumption periods. The goal was to make an analysis on the impacts when betting on WP sources, in order to improve the market interaction with WP integration, considering as criteria the consumer satisfaction, in terms of lower electricity prices and WP availability. For this study, the market bids registered by the Iberian Electricity Market Operator (OMIE), from 2015 to 2017, ran over a new market simulator, specially developed for this proposal, considering a virtual market condition, but not considering the bids made by WP producers. The comparison of the results allowed the evaluation of the WP influence on the EM quantitative, which is noteworthy.

Abstract
Significant wind power ramps have a remarkable influence on the integration of wind power. Their variability and uncertainty affect to the wind power forecast increasing the error and reducing the reliability in the continued operation of the power system. Ramp events are considered the main source of forecasting error and their study is imperative for an improvement of prediction tools. In this aspect, the first steps to achieve a study of the influence are identifying, grouping and temporal characterizing of the ramp events. This paper develops a methodology for wind power ramp events recognition in order to analyze the relationship between these events and the accuracy of the wind power forecast system according with two criteria: maximum forecast deviation and mean magnitude error. The methodology is validated using real data from the highly aggregated Spanish power system and short time timescale forecasting values. © 2016 IEEE.

Abstract
This paper presents the analysis of a novel framework of study and the impact of different market design criterion for the generation expansion planning (GEP) in competitive electricity market incentives, under variable uncertainties in a single year horizon. As investment incentives conventionally consist of firm contracts and capacity payments, in this study, the electricity generation investment problem is considered from a strategic generation company (GENCO)'s perspective, modelled as a bi-level optimization method. The first-level includes decision steps related to investment incentives to maximize the total profit in the planning horizon. The second-level includes optimization steps focusing on maximizing social welfare when the electricity market is regulated for the current horizon. In addition, variable uncertainties, on offering and investment, are modelled using set of different scenarios. The bi-level optimization problem is then converted to a single-level problem and then represented as a mixed integer linear program (MILP) after linearization. The efficiency of the proposed framework is assessed on the MAZANDARAN regional electric company (MREC) transmission network, integral to IRAN interconnected power system for both elastic and inelastic demands. Simulations show the significance of optimizing the firm contract and the capacity payment that encourages the generation investment for peak technology and improves long-term stability of electricity markets.