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The special session on Decision Economics (DECON) is a scientific forum held annually, which is focused on sharing ideas, projects, research results, models, and experiences associated with the complexity of behavioural decision processes and socio-economic phenomena. In 2018, DECON was held at Campus Tecnológico de la Fábrica de Armas, University of Castilla-La Mancha, Toledo, Spain, as part of the 15th International Conference on Distributed Computing and Artificial Intelligence. For the third consecutive year, this book have drawn inspiration from Herbert A. Simon's interdisciplinary legacy and, in particular, is devoted to designs, models, and techniques for boundedly rational decisions, involving several fields of study and expertise. It is worth noting that the recognition of relevant decision-making takes place in a range of critical subject areas and research fields, including economics, finance, information systems, small and international business management, operations, and production. Therefore, decision-making issues are of fundamental importance in all branches of economics addressed with different methodological approaches. As a matter of fact, the study of decision-making has become the focus of intense research efforts, both theoretical and applied, forming a veritable bridge between theory and practice as well as science and business organisations, whose pillars are based on insightful cutting-edge experimental, behavioural, and computational approaches on the one hand, and celebrating the value of science as well as the close relationship between economics and complexity on the other. In this respect, the international scientific community acknowledges Herbert A. Simon's research endeavours to understand the processes involved in economic decision-making and their implications for the advancement of economic professions. Within the field of decision-making, indeed, Simon has become a mainstay of bounded rationality and satisficing. His rejection of the standard (unrealistic) decision-making models adopted by neoclassical economists inspired social scientists worldwide with the purpose to develop research programmes aimed at studying decision-making empirically, experimentally, and computationally. The main achievements concern decision-making for individuals, firms, markets, governments, institutions, and, last but not least, science and research. This book of selected papers tackles these issues that Simon broached in a professional career spanning more than sixty years. The Editors of this book dedicated it to Herb.

The participation of household prosumers in wholesale electricity markets is very limited, considering the minimum participation limit imposed by most market participation rules. The generation capacity of households has been increasing since the installation of distributed generation from renewable sources in their facilities brings advantages for themselves and the system. Due to the growth of self-consumption, network operators have been putting aside the purchase of electricity from households, and there has been a reduction in the price of these transactions. This paper proposes an innovative model that uses the aggregation of households to reach the minimum limits of electricity volume needed to participate in the wholesale market. In this way, the Aggregator represents the community of households in market sales and purchases. An electricity transactions portfolio optimization model is proposed to enable the Aggregator reaching the decisions on which markets to participate to maximize the market negotiation outcomes, considering the day-ahead market, intra-day market, and retail market. A case study is presented, considering the Iberian wholesale electricity market and the Portuguese retail market. A community of 50 prosumers equipped with photovoltaic generators and individual storage systems is used to carry out the experiments. A cost reduction of 6–11% is achieved when the community of households buys and sells electricity in the wholesale market through the Aggregator ; This work has received funding from the European Union's Horizon 2020 research and innovation program under project TradeRES (grant agreement No 864276) and from FEDER Funds through COMPETE program and from National Funds through FCT under projects CEECIND/01811/2017 and UID/EEA/00760/2019 ; info:eu-repo/semantics/publishedVersion

The necessity of end-user engagement in power systems have become a reality in recent times. One of the solutions to this engagement is the creation of local energy markets. The distribution systems operators are compelled to investigate and optimize their asset investment cost in reinforcement of grids by introducing smart grid functionalities in order to avoid investments. The congestion management is the one of the most promising strategies to deal with the network issues. This paper presents a local electricity market or flexibility negotiation as a strategy in order to help the distribution system operator in congestion management. The local market is performed considering an asymmetric action model and is coordinated by an aggregator. A case study is presented considering a simulation that uses a low voltage network with 17 buses, which includes 9 consumers and 3 prosumers, all domestic users. Results show that using the proposed market model, the network congestion is avoided by taking advantage from the trading of consumers flexibility. ; This work has received funding from the European Union's Horizon 2020 research and innovation programme under project DOMINOES (grant agreement No 771066) and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2019 ; info:eu-repo/semantics/publishedVersion

In many large-scale and time-consuming problems, the application of metaheuristics becomes essential, since these methods enable achieving very close solutions to the exact one in a much shorter time. In this work, we address the problem of portfolio optimization applied to electricity markets negotiation. As in a market environment, decision-making is carried out in very short times, the application of the metaheuristics is necessary. This work proposes a Hybrid model, combining a simplified exact resolution of the method, as a means to obtain the initial solution for a Particle Swarm Optimization (PSO) approach. Results show that the presented approach is able to obtain better results in the metaheuristic search process. ; This work has received funding from the European Union's Horizon 2020 research and innovation programme under project DOMINOES (grant agreement No 771066) and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2019 and Ricardo Faia is supported by FCT Funds through and SFRH/BD/133086/2017 PhD scholarship. ; info:eu-repo/semantics/publishedVersion

The portfolio optimization is a well-known problem in the areas of economy and finance. This problem has also become increasingly important in electrical power systems, particularly in the area of electricity markets, mostly due to the growing number of alternative/complementary market types that are being introduced to deal with important issues, such as the massive integration of renewable energy sources in power systems. The optimization of electricity market players' participation portfolio comprises significant time constraints, which cannot be satisfied by the use of deterministic techniques. For this reason, meta-heuristic solutions are used, such as particle swarm optimization. The inertia is one of the most important parameter in this method, and it is the main focus of this paper. This paper studies 18 popular inertia calculation strategies, by comparing their performance in the portfolio optimization problem. A strategic methodology for the automatic selection of the best inertia calculation method for the needs of each optimization is also proposed. Results show that the proposed approach is able to automatically adapt the inertia parameter according to the needs in each execution. ; This work has been developed in the scope of the European Union's Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant agreement No 703689 (project ADAPT) and grant agreement No 641794 (project DREAM-GO); and has also been supported by the CONTEST project – SAICT-POL/23575/2016. Ricardo Faia is supported by FCT Funds through SFRH/BD/133086/2017 (PhD scholarship). ; info:eu-repo/semantics/publishedVersion

This paper proposes a novel hybrid particle swarm optimization methodology to solve the problem of optimal participation in multiple electricity markets. The decision time is usually very important when planning the participation in electricity markets. This environment is characterized by the time available to take action, since different electricity markets have specific rules, which requires participants to be able to adapt and plan their decisions in a short time. Using metaheuristic optimization, participants' time problems can be resolved, because these methods enable problems to be solved in a short time and with good results. This paper proposes a hybrid resolution method, which is based on the particle swarm optimization metaheuristic. An exact mathematical method, which solves a simplified, linearized, version of the problem, is used to generate the initial solution for the metaheuristic approach, with the objective of improving the quality of results without representing a significant increase of the execution time. ; This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 703689 (project ADAPT) and No 641794 (project DREAM-GO); NetEfficity Project (P2020 − 18015); and UID/EEA/00760/2013 funded by FEDER Funds through COMPETE pro-gram and by National Funds through FCT. ; info:eu-repo/semantics/publishedVersion

This paper proposes a coalitional game-theoretical model for consumers' flexibility coalition formation, supported by an optimization model based on differential evolution. Traditionally, the participation in conventional electricity markets used to be limited to large producers and consumers. The final end-users contract their energy supply with retailers, since due to the smaller quantity available for trading, they cannot participate in electricity market transactions. Nowadays, the growing concept of local electricity market brings many advantages to the end-users. The flexibility negotiation considering local areas is an important procedure for network operators and it is incorporating a local electricity market opportunity. A coalition formation model to facilitate small players participation in the flexibility market proposed by the network operator is addressed in this work. The inclusion of Shapley value in the proposed model enables finding the best coalition structures considering the fairness of the coalitions in addition to the potential income achieved by the consumers when selling their flexibility. An optimization model based on differential evolution is also proposed as the way to find the optimal coalition structures based on the multi-criteria specifications. ; This work has received funding from FEDER Funds through COMPETE program and from National Funds through (FCT) under the projects CEECIND/01811/2017 and UIDB/00760/2020, from the European Union's Horizon 2020 research and innovation programme under project DOMINOES (grant agreement No 771066), and Ricardo Faia is supported by national funds through Fundacão para a Ciência e a Tecnologia (FCT) with PhD grant reference SFRH/BD/133086/2017. ; info:eu-repo/semantics/publishedVersion

Smart Grid technologies enable the intelligent integration and management of distributed energy resources. Also, the advanced communication and control capabilities in smart grids facilitate the active participation of aggregators at different levels in the available electricity markets. The portfolio optimization problem consists in finding the optimal bid allocation in the different available markets. In this scenario, the aggregator should be able to provide a solution within a timeframe. Therefore, the application of metaheuristic approaches is justified, since they have proven to be an effective tool to provide near-optimal solutions in acceptable execution times. Among the vast variety of metaheuristics available in the literature, Differential Evolution (DE) is arguably one of the most popular and successful evolutionary algorithms due to its simplicity and effectiveness. In this paper, the use of DE is analyzed for solving the portfolio optimization problem in electricity markets. Moreover, the performance of DE is compared with another powerful metaheuristic, the Particle Swarm optimization (PSO), showing that despite both algorithms provide good results for the problem, DE overcomes PSO in terms of quality of the solutions. ; This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO) and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013 and grant agreement No 703689 (project ADAPT); ; info:eu-repo/semantics/publishedVersion

We have been witnessing a real explosion of information, due in large part to the development in Information and Knowledge Technologies (ICTs). As information is the raw material for the discovery of knowledge, there has been a rapid growth, both in the scientific community and in ICT itself, in the study of the Big Data phenomenon (Kaisler et al., 2014). The concept of Smart Grids (SG) has emerged as a way of rethinking how to produce and consume energy imposed by economic, political and ecological issues (Lund, 2014). To become a reality, SGs must be supported by intelligent and autonomous IT systems to make the right decisions in real time. Knowledge needed for real-time decision-making can only be achieved if SGs are equipped with systems capable of efficiently managing all the surrounding information. Thus, this paper proposes a system for the management of information in the context of SG to enable the monitoring, in real time, of the events that occur in the ecosystem and to predict following events. ; This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO) and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013. ; info:eu-repo/semantics/publishedVersion

The use of metaheuristics to solve real-life problems has increased in recent years since they are easy to implement, and the problems become easy to model when applying metaheuristic approaches. However, arguably the most important aspect is the simulation time since results can be obtained from metaheuristic methods in a much smaller time, and with a good approximation to the results obtained with exact methods. In this work, the Genetic Algorithm (GA) metaheuristic is adapted and apphed to solve the optimization of electricity markets participation portfolios. This work considers a multiobjective model that incorporates the calculation of the profit and the risk incurred in the electricity negotiations. Results of the proposed approach are compared to those achieved with an exact method, and it can be concluded that the proposed GA model can achieve very close results to those of the deterministic approach, in much quicker simulation time. ; This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO) and grant agreement No 703689 (project ADAPT); and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013 ; info:eu-repo/semantics/publishedVersion

This paper addresses the theme automated bilateral negotiation of energy contracts. In this work, the automatic combination between different negotiation tactics is proposed. This combination is done dynamically throughout the negotiation process, as result from the online assessment that is performed after each proposal and counter-proposal. The proposed method is integrated in a decision support system for bilateral negotiations, called Decision Support for Energy Contracts Negotiations (DECON), which in turn is integrated with the Multi-Agent Simulator of Competitive Electricity Markets (MASCEM). This integration enables testing and validating the proposed methodology in a realistic market negotiation environment. A case study is presented, demonstrating the advantages of the proposed approach. ; This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO) and a grant agreement No 703689 (project ADAPT); and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013 ; info:eu-repo/semantics/publishedVersion

This paper proposes a novel Case Based Reasoning (CBR) application for intelligent management of energy resources in residential buildings. The proposed CBR approach enables analyzing the history of previous cases of energy reduction in buildings, and using them to provide a suggestion on the ideal level of energy reduction that should be applied in the consumption of houses. The innovations of the proposed CBR model are the application of the k-Nearest Neighbors algorithm (k-NN) clustering algorithm to identify similar past cases, the adaptation of Particle Swarm Optimization (PSO) meta-heuristic optimization method to optimize the choice of the variables that characterize each case, and the development of expert systems to adapt and refine the final solution. A case study is presented, which considers a knowledge base containing a set of scenarios obtained from the consumption of a residential building. In order to provide a response for a new case, the proposed CBR application selects the most similar cases and elaborates a response, which is provided to the SCADA House Intelligent Management (SHIM) system as input data. SHIM uses this specification to determine the loads that should be reduced in order to fulfill the reduction suggested by the CBR approach. Results show that the proposed approach is capable of suggesting the most adequate levels of reduction for the considered house, without compromising the comfort of the users. ; This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAMGO) and a grant agreement No 703689 (project ADAPT); and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013. ; info:eu-repo/semantics/publishedVersion

Rapid advances in technology make it necessary to prepare our society in every aspect. Some of the most significant technological developments of the last decade are the UAVs (Unnamed Aerial Vehicles) or drones. UAVs provide a wide range of new possibilities and have become a tool that we now use on a daily basis. However, if their use is not controlled, it could entail several risks, which make it necessary to legislate and monitor UAV flights to ensure, inter alia, the security and privacy of all citizens. As a result of this problem, several laws have been passed which seek to regulate their use; however, no proposals have been made with regards to the control of airspace from a technological point of view. This is exactly what we propose in this article: a platform with different modes designed to control UAVs and monitor their status. The features of the proposed platform provide multiple advantages that make the use of UAVs more secure, such as prohibiting UAVs' access to restricted areas or avoiding collisions between vehicles. The platform has been successfully tested in Salamanca, Spain.