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This paper proposes a new methodology for fair remuneration of consumers participation in demand response events. With the increasing penetration of renewable energy sources with a high variability; the flexibility from the consumers' side becomes a crucial asset in power and energy systems. However, determining how to effectively remunerate consumers flexibility in a fair way is a challenging task. Current models tend to apply over-simplistic and non-realistic approaches which do not incentivize the participation of the required players. This paper proposes a novel methodology to remunerate consumers flexibility, in a fair way. The proposed model considers different aggregators, which manage the demand response requests within their coalition. After player provide their flexibility, the remuneration is calculated based on the flexibility amount provided by the players, the previous participation in demand response programs, the localization of the players, the type of consumer, the effort put in the provided flexibility amount, and the contribution to the stability of the coalition structure using the Shapley value. Results show that by assigning different weights to the distinct factors that compose the calculation formulation, players remuneration can be adapted to the needs and goals of both the players and the aggregators. ; 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

Massive changes in electricity markets have occurred during the last years, as a consequence of the massive introduction of renewable energies. These changes have led to a restructuring process that had an impact throughout the electrical industry. The case of the electricity markets is a relevant example, where new forms of trading emerged and new market entities were created. With these changes, the complexity of electricity markets increased as well, which brought out the need from the involved players for adequate support to their decision making process. Artificial intelligence plays an important role in the development of these tools. Multi-agent systems, in particular, have been largely explored by stakeholders in the sector. Artificial intelligence also provides intelligent solutions for optimization, which enable troubleshooting in a short time and with very similar results to those achieved by deterministic techniques, which usually result from very high execution times. The work presented in this paper aims at solving a portfolio optimization problem for electricity markets participation, using an approach based on NPSO-LRS (New Particle Swarm Optimization with Local Random Search). The proposed method is used to assist decisions of electricity market players. ; 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 electricity markets environment has changed completely with the introduction of renewable energy sources in the energy distribution systems. With such alterations, preventing the system from collapsing required the development of tools to avoid system failure. In this new market environment competitiveness increases, new and different power producers have emerged, each of them with different characteristics, although some are shared for all of them, such as the unpredictability. In order to battle the unpredictability, the power supplies of this nature are supported by techniques of artificial intelligence that enables them crucial information for participation in the energy markets. In electricity markets any player aims to get the best profit, but is necessary have knowledge of the future with a degree of confidence leading to possible build successful actions. With optimization techniques based on artificial intelligence it is possible to achieve results in considerable time so that producers are able to optimize their profits from the sale of Electricity. Nowadays, there are many optimization problems where there are no that cannot be solved with exact methods, or where deterministic methods are computationally too complex to implement. Heuristic optimization methods have, thus, become a promising solution. In this paper, a simulated annealing based approach is used to solve the portfolio optimization problem for multiple electricity markets participation. A case study based on real electricity markets data is presented, and the results using the proposed approach are compared to those achieved by a previous implementation using particle swarm optimization. ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 ; info:eu-repo/semantics/publishedVersion

The electric power system has undergone numerous changes over the years. The transformation of the end-users from passive actors to active actors brings implications for the electric power system. The distribution system operator will be able to guide its operations in the function of the active role of the end-users. In many situations, the distribution system operator is carried out to avoid congestion in the distribution networks, and when it happens the distribution system operator is obliged to compensate the affected end-users. This paper presents a model in which distribution system operator can take advantage of the flexibility of the end-users in order to minimize the costs of the investments in distribution network expansion. The investment cost with the presented methodology as show the results has a reduction of 5.77%. ; 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. Ricardo Faia is supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with PhD grant reference SFRH/BD/133086/2017 and Bruno Canizes is supported by FCT Funds through SFRH/BD/110678/2015 PhD scholarship ; 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

Local energy markets (LM) are attracting significant interest due to their potential of balancing generation and consumption and supporting the adoption of distributed renewable sources at the distribution level. Besides, LMs aim at increasing the participation of small end-users in energy transactions, setting the stage for transactive energy systems. In this work, we explore the use of ant colony optimization (ACO) for learning bidding strategies under a bi-level optimization framework that arises when trading energy in an LM. We performed an empirical analysis of the impact of ACO parameters have in the learning process and the obtained profits of agents. After that, we analyze and compare ACO performance against an evolutionary algorithm under a realistic case study with nine agents trading energy in the day-ahead LM. Results suggest that ACO can be efficient for strategic learning of agents, providing solutions in which all agents can improve their profits. Overall, it is shown the advantages that an LM can bring to market participants, thereby increasing the tolerable penetration of renewable resources and facilitating the energy transition. ; This work has received funding from the European Union's Horizon 2020 research and innovation programme under project DOMINOES (grant agreement No 771066) and CENERGETIC (POCI-01-0145- FEDER-028983 and PTDC/EEI-EEE/28983/2017), from FEDER Funds through COMPETE program and from National Funds through (FCT) under the project UIDB/00760/2020, and grants CEECIND/02814/2017, CEECIND/02887/2017, SFRH/BD/133086/2017. ; info:eu-repo/semantics/publishedVersion

This paper proposes a case-based reasoning methodology to automatically choose the most appropriate optimization algorithms and respective parameterizations to solve the problem of optimal resource scheduling in smart energy grids. The optimal resource scheduling is, however, a heavy computation problem, which deals with a large number of variables. Moreover, depending on the time horizon of this optimization, fast response times are usually required, which makes it impossible to apply traditional exact optimization methods. For this reason, the application of metaheuristic methods is the natural solution, providing near-optimal solutions in a much faster execution time. Choosing which optimization approaches to apply in each time is the focus of this work, considering the requirements for each problem and the information of previous executions. A case-based reasoning methodology is proposed, considering previous cases of execution of different optimization approaches for different problems. A fuzzy logic approach is used to adapt the solutions considering the balance between execution time and quality of results ; This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 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 (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

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

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