Abstract
Electric vehicles (EVs) promote many advantages for distribution systems such as increasing efficiency and reliability, decreasing dependence on non-endogenous resources, and reducing pollutant emissions. Due to increased proliferation of EVs and their integration in power systems, management and operation of distribution systems (ODS) is becoming more important. Recent studies have shown that EV can increase power grid flexibility since EV owners do not use them for 93-96% of the daytime. Therefore, it is important to exploit parking time, during which EVs can act either as a load or distributed storage device, to maximize the benefit for the power system. Following a survey of the current state-of-the-art, this work studies the impact of EV charging on the load profile. Since renewable energy resources (RES) play a critical role in future distribution systems the current case study considered the presence of RES and their stochastic nature has been modeled. The study proceeds with analyzing EV owners' driving habits, enabling prediction of the network load profile. The impact of: EV charging modes (i.e., controlled and uncontrolled charging), magnitude of wind and photovoltaic (PV) generation, number of EVs (penetration), and driving patterns on the ODS is analyzed.

Abstract
During recent decades with the power system restructuring process, centralized energy sources are being replaced with decentralized ones. This phenomenon has resulted in a novel concept in electric power systems, particularly in distribution systems, known as Distributed Generation (DG). On one hand, utilizing DG is important for secure power generation and reducing power losses. On the other hand, widespread use of such technologies introduces new challenges to power systems such as their optimal location, protection devices' settings, voltage regulation, and Power Quality (PQ) issues. Another key point which needs to be considered relates to specific DG technologies based on Renewable Energy Sources (RESs), such as wind and solar, due to their uncertain power generation. In this regard, this paper provides a comprehensive review of different types of DG and investigates the newly emerging challenges arising in the presence of DG in electrical grids.

Abstract
The integration of renewable energy resources (RES) (such as wind and photovoltaic (PV)) on large or small scales, in addition to small generation units, and individual producers, has led to a large variation in energy production, adding uncertainty to power systems (PS) due to the inherent stochasticity of natural resources. The implementation of demand-side management (DSM) in distribution grids (DGs), enabled by intelligent electrical devices and advanced communication infrastructures, ensures safer and more economical operation, giving more flexibility to the intelligent smart grid (SG), and consequently reducing pollutant emissions. Consumers play an active and key role in modern SG as small producers, using RES or through participation in demand response (DR) programs. In this work, the proposed DSM model follows a two-stage stochastic approach to deal with uncertainties associated with RES (wind and PV) together with demand response aggregators (DRA). Three types of DR strategies offered to consumers are compared. Nine test cases are modeled, simulated, and compared in order to analyze the effects of the different DR strategies. The purpose of this work is to minimize DG operating costs from the Distribution System Operator (DSO) point-of-view, through the analysis of different levels of DRA presence, DR strategies, and price variations.

Abstract
In recent years, there have been notable commitments and obligations by the electricity sector for more sustainable generation and delivery processes to reduce the environmental footprint. However, there is still a long way to go to achieve necessary sustainability goals while ensuring standards of robustness and the quality of power grids. One of the main challenges hindering this progress are uncertainties and stochasticity associated with the electricity sector and especially renewable generation. In this paradigm shift, forecasting tools are indispensable, and their utilization can significantly improve system operation and minimize costs associated with all related activities. Thus, forecasting tools have an essential key role in all decision-making stages. In this work, a hybrid probabilistic forecasting model (HPFM) was developed for short-term electricity market prices (EMP) combining wavelet transforms (WT), hybrid particle swarm optimization (DEEPSO), adaptive neuro-fuzzy inference system (ANFIS), and Monte Carlo simulation (MCS). The proposed hybrid probabilistic forecasting model (HPFM) was tested and validated with real data from the Spanish and Pennsylvania-New Jersey-Maryland (PJM) markets. The proposed model exhibited favorable results and performance in comparison with previously published work considering electricity market prices (EMP) data, which is notable.

Abstract
In the past two decades, interest in demand response (DR) schemes has grown exponentially. The need for DR has been driven by sustainability (environmental and socioeconomic) and cost-efficiency. The main premise of DR is to influence the timing and magnitude of consumption to match energy supply by sharing the benefits with consumers, ultimately aiming to optimize generation cost. As such, the first and primary enabler to DR was the establishment of contemporary electricity markets. Increased proliferation of Distributed Energy Resources (DER) and microgeneration further motivated the participation of consumers as active players in the market, popularizing DR and the wider category of Demand-Side Management (DSM) programs. Smart Grids (SG) have been an enabler to modern DR schemes, with smart metering data providing input to the underlying optimization and forecasting tools. The more recent emergence of the Internet of Energy (IoE), seen as the evolution of SG, is driven by increased Internet of Things (IoT)-enabling and high penetration of scalable and distributed energy resources. In this IoE paradigm being a fully decentralized network of energy prosumers, DR will continue to be a vital aspect of the grid in future Transactive Energy (TE) schemes, aiming for a more user-centered, energy-efficient, cost-saving, energy management approach. This paper investigates original motives and identifies the first mentions of DR in the legislative and scientific literature. Afterwards, the evolution of DR is tracked over the past four decades, attempting to study the co-influence of legislation and research by performing a thorough statistical analysis of research trends on the IEEE Xplore digital library. Finally, conclusions are made as to the current state of DR and future prospects of DR are discussed.

Abstract
With the integration of new power production technologies and the growing focus on dispersed production, there has been a paradigm change in the electricity sector, mostly under a renewable and sustainable way. Consequentially, challenges for profitability as well as correct management of the electricity sector have increased its complexity. The use of forecasting tools that allow a real and robust approach makes it possible to improve system operation and thus minimizing costs associated with the activities of the electric sector. Hence, the forecasting approaches have an essential role in all stages of the electricity markets. In this paper, a hybrid probabilistic forecasting model (HPFM) was developed for short-term electricity market prices (EMP), combining Wavelet Transform (WT), hybrid particle swarm optimization (DEEPSO), Adaptive Neuro-Fuzzy Inference System (ANFIS), together with Monte Carlo Simulation (MCS). The proposed HPFM was tested and validated with real data from the Spanish and Pennsylvania-New Jersey-Maryland (PJM) markets, considering the next week ahead. The model was validated by comparing the results with previously published results using other methods.