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
This article presents original Probabilistic Price Forecasting Models, for day-ahead hourly price forecasts in electricity markets, based on a Nadaraya-Watson Kernel Density Estimator approach. A Gaussian Kernel Density Estimator function is used for each input variable, which allows to calculate the parameters of the probability density function (PDF) of a Beta distribution for the hourly price variable. Thus, valuable information is obtained from PDFs such as point forecasts, variance values, quantiles, probabilities of prices, and time series representations of forecast uncertainty. A Reliability Indicator is also introduced to give a measure of "reliability" of forecasts. The Probabilistic Price Forecasting Models were satisfactorily applied to the real-world case study of the Iberian Electricity Market. Input variables of these models include recent prices, power demands and power generations in the previous day, power demands in the previous week, forecasts of demand, wind power generation and weather for the day-ahead, and chronological data. The best model, corresponding to the best combination of input variables that achieves the lowest MAE, obtains one of the highest Reliability Indicator values. A systematic analysis of MAE values of the Probabilistic Price Forecasting Models for different combinations of input variables showed that as more types of input variables were considered in these models, MAE values improved and Reliability Indicator values usually increased.

Abstract
This article presents original probabilistic price forecasting meta-models (PPFMCP models), by aggregation of competitive predictors, for day-ahead hourly probabilistic price forecasting. The best twenty predictors of the EEM2016 EPF competition are used to create ensembles of hourly spot price forecasts. For each hour, the parameter values of the probability density function (PDF) of a Beta distribution for the output variable (hourly price) can be directly obtained from the expected and variance values associated to the ensemble for such hour, using three aggregation strategies of predictor forecasts corresponding to three PPFMCP models. A Reliability Indicator (RI) and a Loss function Indicator (LI) are also introduced to give a measure of uncertainty of probabilistic price forecasts. The three PPFMCP models were satisfactorily applied to the real-world case study of the Iberian Electricity Market (MIBEL). Results from PPFMCP models showed that PPFMCP model 2, which uses aggregation by weight values according to daily ranks of predictors, was the best probabilistic meta-model from a point of view of mean absolute errors, as well as of RI and LI. PPFMCP model 1, which uses the averaging of predictor forecasts, was the second best meta-model. PPFMCP models allow evaluations of risk decisions based on the price to be made.

Abstract

Abstract
The development of the smart grid concept implies major changes in the operation and planning of distribution systems, particularly in Low Voltage (LV) networks. The majority of small-scale Distributed Energy Resources (DER)-microgeneration units, energy storage devices, and flexible loads-are connected to LV networks, requiring local control solutions to mitigate technical problems resulting from its integration in the system. Simultaneously, LV connected DER can be aggregated in small cells in order to globally provide new functionalities to system operators. Within this view, the Microgrid (MG) concept has been pointed out as a solution to extend and decentralize the distribution network monitoring and control capability. An MG is a highly flexible, active, and controllable LV cell, incorporating microgeneration units based on Renewable Energy Sources (RES) or low carbon technologies for small-scale combined heat and power applications, energy storage devices, and loads. The coordination of MG local resources, achieved through an appropriated network of controllers and communication system, endows the LV system with sufficient autonomy to operate interconnected to the upstream network or autonomously-emergency operation. In this case, the potentialities of DER can be truly realized if the islanded operation is allowed and bottom-up black start functionalities are implemented. To achieve this operational capability, this chapter presents the control procedures to be used in such a system to deal with the islanded operation and to exploit the local generation resources as a way to help in power system restoration in case of an emergency situation. A sequence of actions for a black start procedure is identified, and it is expected to be an advantage for power system operation regarding reliability as a result from the presence of a huge amount of dispersed generation.

Abstract
A microgrid embraces a low-voltage (LV) distribution grid with distributed energy resources (DER) and controllable loads. In the last years, there has been a growing awareness in exploiting microgrids to facilitate DER integration in electric power systems as well as to improve reliability and power quality in distribution grids. A microgrid can operate connected to the upstream medium voltage (MV) grid-utility grid-or islanded (disconnected from the MV grid) in a controlled and coordinated way. A major challenge associated with the implementation of microgrids is to design a suitable protection system scheme for different operating conditions. To overcome this challenge, different approaches have been proposed in the literature. The protection systems applied at microgrids must work both in utility grid faults and microgrid faults. Faults on the utility grid could lead to a protection response that isolates the microgrid from the utility grid as fast as required to keep the microgrid safety. On the other hand, faults in the own microgrid require the smallest sector removal of the microgrid to isolate the fault. Due to the presence of several DER in microgrids, the protection systems are also needed to cope with the bidirectional energy flows. Thus, the traditional protection devices (fuses and electromechanical switches) and standard solid-state relays are designed for selectivity purposes, making them inapt to ensure the protection of microgrids. These protection devices do not provide flexibility for setting the tripping characteristics neither the current direction sensitivity feature. Some problems related to protections sensitivity and selectivity arises when a microgrid is in islanded operation (DER generation). Thus, this new paradigm of distribution facilities requires a protection system based on microprocessor relaying and communications. Protecting microgrids in both modes (grid-connected and islanded) can be achieved by using different communication architectures associated with protections. Using centralized or distributed architectures means that the relay protection settings are modified centrally or locally regarding microgrid operating conditions. This chapter aims to provide the key highlights of the available protection schemes used to address microgrid protection issues.