Abstract
The environmental targets set by power sectors throughout the world are the main drivers toward increasing the share of variable renewable energy sources (VRESs). Growth of VRESs will lead to a higher demand for operational flexibility due to their stochastic nature. Traditionally, conventional generation units provide the major share of additional required flexibility that may result in a higher depreciation. Motivated by this challenge, this paper investigates the potential of Demand Response (DR) as an emerging alternative in systems with significant amounts of wind power. To this end, a comprehensive set of DR programs including tariff-based, incentive-based and combinational DR programs are considered in a stochastic network-constrained market clearing framework. Afterwards, various DR programs are prioritized taking into account the system operator's economic, technical, and environmental desires. Moreover, the sensitivity of different DR programs into customer's price elasticity of demand as well as the participation level are evaluated by means of several sensitivity analyses. The obtained results can provide a guideline for the system operators to opt the most effective DR program.

Abstract
The effects of optimal dimensioning and integration of distributed generation (DG) on an electricity distribution system (DS) from a probabilistic viewpoint is presented in this paper, as a new contribution to earlier studies. The proposed methodology pays special attention to preventing reverse power flow at substation as a consequence of excessive integration of renewable energy based DG. As the analysis of large amounts of data typically measured on an annual basis could be exhausting from a computational perspective, a methodology based on estimating the potential of wind and solar resources is applied; from this procedure, those months of highest renewable potential are selected so that indirectly those situations with probability of reverse power flow at substation are considered. After this, time series of load demand per node and phase are generated using typical profiles and the corresponding peak-load expected. Finally, all this information is introduced on an optimization algorithm based on a genetic algorithm in order to minimize the net present cost over the project lifetime, obtaining the type and number of photovoltaic (PV) panels and wind turbines (WTs) to be installed. This approach allows integrating detailed mathematical models of DG related to PV and wind generation, including specific factors frequently reported by the manufacturers such as temperature coefficients, nominal operating cell temperature, particular WT power curves, and variable efficiency of power converter, among other characteristics. The proposed method is illustrated by studying a DS supposed to be located in Zaragoza, Spain, with 35 nodes under unbalanced conditions, with residential as well as small, medium, and large commercial electricity demands. Focusing our attention on the month of February, due to its high renewable potential, the proposed technique was applied resulting in a system mainly based on wind energy of at least 40% of the substation capacity. This model could be used to perform the renewable energy integration analysis on DS, starting from typical load profiles, hourly estimations of solar and wind resources, and data frequently provided by PV panels and WT manufacturers.

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.

Abstract
A large quantity of variable renewable energy sources (RESs), most notably wind and solar, is now connected to the Portuguese network system, which makes it somehow unique. Yet, in the coming years, the network is expected to accommodate more of these and other technologies of "clean" power productions. The deployment and efficient utilization of various flexibility options are certainly required in a system experiencing such levels of dynamic changes so as to ensure a standard level of service provision in terms of security, stability and reliability. Among these is a battery energy storage system (BESS), which is emerging as one of the most viable and effective options of increasing the much-needed flexibility in power systems. This work aims to assess the impact of deploying BESSs on the operational performance of the Portuguese transmission grid, mainly in terms operational flexibility and variable RES power support. In particular, the potential benefits of strategically placed BESSs are investigated using a stochastic optimization framework. Numerical results show that integrating BESSs leads to a more efficient use of renewable power by considerably minimizing curtailments, and a 10% reduction in system-wide cost. Energy losses are moderately increased as a result of the BESS deployment. But this is offset by the savings in operation and emission costs.

Abstract
Today's power systems are subject to various challenges arising from the large-scale integration of renewable energy sources (RES), especially wind energy production. System flexibility, or the capability of a system to address deviations in variable RES production, is becoming more and more relevant. This paper aims to provide a systematic approach to evaluate the level of flexibility of a power system by unequivocally considering fast-ramping units (FRU), hourly demand response (DR) and energy storage (ES). In addition, to research the flexibility role in power system operation, an "online" index is considered to evaluate the technical aptitude of the FRU, hourly DR and ES system to deliver the required flexibility. The mathematical representation of day-ahead scheduling, with the added modeling of an online flexibility index, is a mixed-integer nonlinear program (MINLP). This paper presents a method to convert this MINLP into a mixed-integer linear program without loss of accuracy. The adapted 6-bus and IEEE 118-bus systems are employed to assess the suggested models and flexibility metric, demonstrating the proficiency of the online flexibility index.