Abstract
Renewable energy sources have transformed the electricity market, allowing virtual power players or aggregators to participate and benefit from selling surplus energy. However, meeting demand and ensuring energy production stability can be challenging due to the intermittent nature of renewable sources. Accurate forecasting of energy consumption, generation, and electricity prices is critical to address these issues. Moreover, the selection of the best algorithm for forecasting is usually based on the predictions’ accuracy, neglecting other factors such as the impact of errors on the real context. This paper presents a study on the economic risk of price forecasting errors on the electricity market’s trading. For this, a simulation model is proposed to analyze the deviations between actual and predicted prices and how these deviations can affect trading in the electricity market, where the main purpose is to maximize profit, depending on whether the player is buying or selling electricity. The economic risk analysis and the predictions scores are used to improve the forecasts, using an approach based on reinforcement learning to evaluating and selecting which models demonstrated better performance in past transactions. The study involved simulating an aggregator’s transactions in the Iberian electricity market for two consecutive days in October 2021. Real data from the market operator between 2020 and 2021 and seven forecasting models were used. The findings showed that errors have a significant impact on profit. Including the economic impact analysis and score evaluation of forecasting methods to determine which method can offer better results has proven to be a viable approach.

Abstract
In recent years, sustainable power supply has become a necessary asset for the daily survival and development of populations. The incentive to the use of renewable energies has been increasing worldwide. Solar energy, in particular, is widespreading fast in countries whose location allows to obtain excellent radiation conditions. In this work, autonomous photovoltaic (PV) systems are studied, having as main aim its application in the supply of urban loads. For this purpose, a PV system is designed to supply the decorative lighting of a monument. Particular emphasis is given to studying the behavior of the energy storage system. The achieved results demonstrate that the use of this type of systems is a very efficient solution for the municipalities to supply several urban loads such as fountains, traffic lights, decorative lighting, among others.

Abstract
Worldwide power and energy systems are changing significantly [...]

Abstract
There is a growing complexity, volatility, and unpredictability in the electric sector that hardens the decision-making process. To this end, the use of proper decision support tools and simulation platforms becomes essential. This paper presents the Multi-Agent based Real-Time INfrastructure for Energy (MARTINE) platform that allows real-time simulation and emulation of loads, resources, and infrastructures. MARTINE uses multi-agent systems that connect to physical resources and can represent additional simulated players that are not physically present in the simulation and emulation environment, enabling the creation of complex scenarios for testing and validation. MARTINE provides the seamless integration of real-time emulation with simulated and physical resources simultaneously in a unique simulation environment, which is only possible by supporting multi-agent systems. This work presents MARTINE's integration in a semantically interoperable multi-agent systems society developed for the test, study, monitoring, and validation of the power system sector. The use of ontologies and semantic web technologies eases the interoperability between the heterogeneous systems. The case study scenario demonstrates the use of MARTINE in simulating a local community electricity market that combines real-time data from physical devices with simulated data and the use of semantic web techniques to make the system interoperable, configurable, and flexible.& COPY; 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Abstract
Resumo:Com este estudo apresenta-se uma nova perspectiva tridimensional do Castelo Velho de Safara (Moura), um dos grandes povoados muralhados da Idade do Ferro/período Romano Republicano existentes na linha do Rio Guadiana, criada a partir de um levantamento de alta resolução realizado com sensor LiDAR acoplado a um drone. O resultado obtido permitiu a definição detalhada da planta da muralha, o que implicou a revisão da superfície de ocupação, que agora é estimada em cerca de 1.36 hectares. Foram também identificados vários elementos inéditos, como a zona da entrada e eventuais soluções de reforço defensivo. Os dados obtidos para o Castelo Velho de Safara demonstram o potencial dos dados LiDAR para o reconhecimento das características topográficas deste tipo de recintos fortificados, cuja visibilidade das estruturas nem sempre é possível a olho nu.Palavras-chave: Idade do Ferro; Período Romano Republicano; Estruturas defensivas; Drone; LiDAR.   Abstract:This study presents a new tridimensional perspective on Castelo Velho de Safara (Moura), one of the great walled settlements of the Iron Age/Roman Republic by the Guadiana River, obtained through a high-resolution survey using a drone integrated with a LiDAR sensor. The outline of the walls was defined in more detail, which meant revising the occupation area, now estimated at circa 1.36 hectares. Several unknown elements were detected, such as the entrance area and other possible defensive structures. The data obtained for the Castelo Velho de Safara demonstrate the potential of LiDAR for understanding the topographical characteristics of this type of fortified enclosure, whose structural remains are not always clear to the naked eye.Keywords: Iron Age; Roman Republic; Defensive structures; Drone; LiDAR; Odyssey.

Abstract
This study presents a new tridimensional perspective on Castelo Velho de Safara (Moura), one of the great walled settlements of the Iron Age/Roman Republic by the Guadiana River, obtained through a high-resolution survey using a drone integrated with a LiDAR sensor. The outline of the walls was defined in more detail, which meant revising the occupation area, now estimated at circa 1.36 hectares. Several unknown elements were detected, such as the entrance area and other possible defensive structures. The data obtained for the Castelo Velho de Safara demonstrate the potential of LiDAR for understanding the topographical characteristics of this type of fortified enclosure, whose structural remains are not always clear to the naked eye. © 2023, APEQ - Associacao Portuguesa para o Estudo do Quaternario. All rights reserved.