Abstract
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Abstract
This paper proposes a decision support model to define electricity consumers' remuneration structures when providing consumption flexibility, optimized for different load regimes. The proposed model addresses the remuneration of consumers when participating in demand response programs, benefiting or penalizing those who adjust their consumption when needed. The model defines dynamic remuneration values with different natures for the aggregator (e.g. flexibility aggregator or curtailment service provider) and for the consumer. The preferences and perspective of both are considered, by incorporating variables that represent the energy price, the energy production and the flexibility of consumers. The validation is performed using real data from the Iberian market, and results enable to conclude that the proposed model adapts the remuneration values in a way that it is increased according to the consumers' elastic, while being reduced when the generation is higher. Consequently, the model boosts the active consumer participation when flexibility is required, while reaching a solution that represents an acceptable g tradeoff between the aggregators and the consumers. (C) 2022 The Authors. Published by Elsevier Ltd.

Abstract
In the upcoming years, European countries have to make a strong bet on solar energy. Small photovoltaic systems are able to provide energy for several applications like housing, traffic and street lighting, among others. This field is expected to have a big growth, thus taking advantage of the largest renewable energy source existing on the planet, the sun. This paper proposes a computational model able to simulate the behavior of a stand-alone photovoltaic system. The developed model allows to predict PV systems behavior, constituted by the panels, storage system, charge controller and inverter, having as input data the solar radiation and the temperature of the installation site. Several tests are presented that validates the reliability of the developed model.

Abstract
The large-scale integration of electric vehicles (EVs) can contribute to the better use of renewable resources and the emergence of new technologies. However, if not properly controlled, it has several downsides. Several strategies make it possible to perform this control by making use of data mining models to deal with the large amounts of data associated with EVs that need to be considered. Accordingly, this chapter presents a study on the progress of EVs integration, where the economic and socio-demographic aspects and the development of the EVs global market are highlighted. Furthermore, some recommendations are suggested to policymakers related to EV management and possibilities for future improvement of EV integration. Finally, this chapter provides a review of data mining models and applications that deal, directly or indirectly, with EV-related problems. © 2023 The Institute of Electrical and Electronics Engineers, Inc.

Abstract
Data mining approaches are increasingly important to enable dealing with the constantly rising challenges in power and energy systems. Classification models, in particular, are suitable for predicting classes of new observations based on previous cases. This chapter illustrates the advantages of the use of classification models, namely artificial neural networks and support vector machines, to predict the behavior profiles of electricity market negotiation players. A clustering model is used to identify similarities in the behavior of players, resulting in a set of negotiation profiles. The negotiation behavior of new players is then classified as belonging to one of these profiles, allowing for an automated adaptation of the negotiation process according to the expected reactions of the opponent. © 2023 The Institute of Electrical and Electronics Engineers, Inc.

Abstract
The rapid developments in Internet-of-Things (IoT), cloud computing, and big data technologies have increased the popularity of machine learning (ML) techniques. As a result, of all ML techniques, deep learning (DL) is at the forefront of innovation, outperforming all other techniques in many application domains. DL has made breakthroughs in speech recognition, image processing, forecasting, natural language processing, fault detection, power disturbance classification, energy trading, and much more. DL is a complex ML approach composed of multiple processing layers, which allows pattern and structure recognition on huge datasets. This chapter takes an in-depth look at the most recent and promising DL works in the literature for intelligent power and energy systems (PES). Several types of problems are explored, including regression, classification, and decision-making problems. The presented works show an increasing trend of new DL techniques that outperform traditional approaches, either through novel architectures or hybrid systems. © 2023 The Institute of Electrical and Electronics Engineers, Inc.