Abstract
A control strategy Is proposed In this paper based on considering the upper and lower's arms modulation functions of a MMC in HVDC applications. The designed modulation-functions-based controller is consisted of the modulation index and phase that are accurately evaluated according to ac-side voltage, MMC voltage and current components in a-b-c reference frame. Two main contributions of the proposed control strategy over the other existing control techniques are robust against the MMC parameter variations and simplicity operation that causes MMC to perform the ac/DC conversion for the HVDC applications. The simulation results verify the ability of the proposed control strategy at approaching to the stable performance of MMC under various operating conditions. © 2018 IEEE.

Abstract
The comprehensive understanding of the residential electricity consumption patterns (ECPs) and how they relate to household characteristics can contribute to energy efficiency improvement and electricity consumption reduction in the residential sector. After recognizing the limitations of current studies (i.e. unreasonable typical ECP (TECP) extraction method and the problem of multicollinearity and interpretability for regression and machine learning models), this paper proposes an association rule mining based quantitative analysis approach of household characteristics impact on residential ECPs trying to address them together. First, an adaptive density-based spatial clustering of applications with noise (DBSCAN) algorithm is utilized to create seasonal TECP of each individual customer only for weekdays. K-means is then adopted to group all the TECPs into several clusters. An enhanced Apriori algorithm is proposed to reveal the relationships between TECPs and thirty five factors covering four categories of household characteristics including dwelling characteristics, socio-demographic, appliances and heating and attitudes towards energy. Results of the case study using 3326 records containing smart metering data and survey information in Ireland suggest that socio-demographic and cooking related factors such as employment status, occupants and whether cook by electricity have strong significant associations with TECPs, while attitudes related factors almost have no effect on TECPs. The results also indicate that those households with more than one person are more likely to change ECP across seasons. The proposed approach and the findings of this study can help to support decisions about how to reduce electricity consumption and CO2 emissions.

Abstract
The large-scale integration of photovoltaic generation (PVG) on distribution systems (DSs) preserving their technical constraints related to voltage fluctuations and active power (AP) flow is a challenging problem. Solar resources are accompanied by uncertainty regarding their estimation and intrinsically variable nature. This study presents a new probabilistic methodology based on quasi-static time-series analysis combined with the golden section search algorithm to integrate low and high levels of PVG into DSs to prevent AP flow in reverse direction. Based on the analysis of two illustrative case studies, it was concluded that the successful integration of PVG is not only related to the photovoltaic-cell manufacturing prices and conversion efficiency but also with the manufacturing prices of power electronic devices required for reactive power control.

Abstract
In smart grid, demand response (DR) programs can be deployed to encourage electricity consumers towards scheduling their controllable demands to off-peak periods. Motivating the consumers to participate in a DR program is a challenging task, as they experience a confidential discomfort cost by modifying their load demand from the desirable pattern to the scheduled pattern. Meanwhile, to balance the load and generation, the independent system operator (ISO) requires to motivate the suppliers towards modifying their generation profiles to follow the changes in the load demands. Additionally, to protect the entities' privacy, the ISO needs to apply an effective well-designed pricing scheme. In this paper, we focus on proposing a decentralized DR framework considering the operating constraints of the grid. In our proposed framework, each individual entity responds to the control signals called conjectured prices from the ISO to modify its demand or generation profile with the locally-available information. We formulate the centralized problem of the ISO that jointly minimizes the suppliers' generation cost and the consumers' discomfort cost. We also discuss how the ISO determines the conjectured prices to motivate the entities toward an operating point that coincides with the solution to the centralized problem. The performance of the proposed algorithm is evaluated on a modified IEEE 14-bus in reducing the suppliers' and consumers' cost, as well as the transmission lines congestion.

Abstract
Demand response (DR) is one of the most cost-effective elements of residential and small industrial building for the purpose of reducing the cost of energy. Today with broadening of the smart grid, electricity market and especially smart home, using DR can reduce cost and even make profits for consumers. On the other hand, utilizing centralized controls and have bidirectional communications Bi-directional communication between DR aggregators and consumers make many problems such as scalability and privacy violation. In this chapter, we propose a multi-agent method based on a Q-learning algorithm Q-learning algorithm for decentralized control of DR. Q-learning is a model-free reinforcement learning Reinforcement learning technique and a simple way for agents to learn how to act optimally in controlled Markovian domains. With this method, each consumer adapts its bidding and buying strategy over time according to the market outcomes. We consider energy supply for consumers such as small-scale renewable energy generators. We compare the result of the proposed method with a centralized aggregator-based approach that shows the effectiveness of the proposed decentralized DR market Decentralized DR market. © Springer International Publishing AG, part of Springer Nature 2018.

Abstract
With the advent of restructuring electricity sector and smart grids, combined with the increased variability and uncertainty associated with electricity market prices (EMP) signals and players' behavior, together with the growing integration of renewable energy sources, enhancing prediction tools are required for players and different regulators agents to face the non-stationarity and stochastic nature of such time series, which must be capable of supporting decisions in a competitive environment with low prediction error, acceptable computational time and low computational complexity. Hybrid and evolutionary approaches are good candidates to surpass most of the previous concern considering time series prediction. In this sense, this work proposes a hybrid model composed by a novel combination of differential evolutionary particle swarm optimization (DEEPSO) and adaptive neuro-fuzzy inference system (ANFIS) to predict, in the short-term, the wind power and EMP, testing its results with real and published case studies, proving its superior performance within a robust prediction software tool. © 2016 IEEE.