Abstract
Environmental pollution and greenhouse gas emissions, as well as oil depletion are prime motivators for the development and adoption of renewable power sources. However, the traditional operating philosophy of power systems and the random nature of these sources represent an important barrier for their massive deployment. Fluctuations of wind and solar power generation integrated as distributed sources can induce important variations on the voltage profile, leading to values out of the range typically suggested by power quality standards. To deal with this problem, in this paper the optimal setting of voltage regulators (VRs) at each hour has been optimally determined by implementing a genetic algorithm with integer codification, so that it can be effectively integrated with the load flow methodologies currently available in the literature without requiring any linearization process. Results obtained from the analysis of a case study reveal the behavior of the optimal VR settings and reactive power compensation on a daily basis, which are highly correlated with the daily load profile.

Abstract
A real-time price (RTP)-based automatic demand response (ADR) strategy for PV-assisted electric vehicle (EV) Charging Station (PVCS) without vehicle to grid is proposed. The charging process is modeled as a dynamic linear program instead of the normal day-ahead and real-time regulation strategy, to capture the advantages of both global and real-time optimization. Different from conventional price forecasting algorithms, a dynamic price vector formation model is proposed based on a clustering algorithm to form an RTP vector for a particular day. A dynamic feasible energy demand region (DFEDR) model considering grid voltage profiles is designed to calculate the lower and upper bounds. A deduction method is proposed to deal with the unknown information of future intervals, such as the actual stochastic arrival and departure times of EVs, which make the DFEDR model suitable for global optimization. Finally, both the comparative cases articulate the advantages of the developed methods and the validity in reducing electricity costs, mitigating peak charging demand, and improving PV self-consumption of the proposed strategy are verified through simulation scenarios.

Abstract
Smart grid is a recently growing area of research including optimum and reliable operation of bulk power grid from production to end-user premises. Demand side activities like demand response (DR) for enabling consumer participation are also vital points for a smarter operation of the electric power grid. For DR activities in end-user level regulated by energy management systems, a dynamic price variation determined by optimum operating strategies should be provided aiming to shift peak demand periods to off-peak periods of energy usage. In this regard, an optimum generation scheduling based price making strategy is evaluated in this paper together with the analysis of the impacts of dynamic pricing on demand patterns with case studies. Thus, the importance of considering DR based demand pattern changes on price making strategy is presented for day-ahead energy market structure.

Abstract
Industrial consumers have traditionally provided flexibility for power systems through various Demand Response (DR) programs in different regions of the world, but below their real potential. Utilizing DR in industries will reduce the need for more expensive alternative forms of flexibility like storage or backup plants. In current increasingly flexible electricity market, it is an excellent chance for the industrial sites to carry out the most of energy management with DR, especially for those plants that are already equipped with the required facilities for DR. This paper aims at providing a comprehensive review of applications of DR in the industrial sector. On this basis, this survey firstly presents the contribution of ancillary services and their potential in industries and then introduces different types of industries with higher potential for DR programs. Finally, the main barriers that hinder the widespread utilization of these programs in industries are presented and categorized.

Abstract
This paper presents an assessment of baseline and higher order grid security criterion applications on current power grid systems, with a focus on their implications in an island context. It considers the European grid code requirements for N-1 and higher order criteria as essential elements for the future deployment of insular distribution grid systems to improve security and stability. Consequently, in the opening section, major facts about island grid topology and current security challenges are illustrated based on a literature review. Afterwards, different approaches applied for security assessment are discussed. A summary of optimal power flow (OPF) approaches is given based on classic problem formulation, and a distribution grid security constrained assessment algorithm is proposed and assessed for island grid context.

Abstract
In this paper a novel scenario generation methodology based on artificial neural networks (ANNs) is proposed. The methodology is able to create scenarios for various power system-related stochastic variables. Scenario reduction methodologies can then be applied to effectively reduce the number of scenarios. An application of the methodology for the creation of short-term electric load scenarios for one day up to seven days ahead is presented. Test results on the real-world insular power system of Crete present the effectiveness of the proposed methodology.