Abstract
This paper presents the demonstration of an energy resources management approach using a physical smart city model environment. Several factors from the industry, governments and society are creating the demand for smart cities. In this scope, smart grids focus on the intelligent management of energy resources in a way that the use of energy from renewable sources can be maximized, and that the final consumers can feel the positive effects of less expensive (and pollutant) energy sources, namely in their energy bills. A large amount of work is being developed in the energy resources management domain, but an effective and realistic experimentation are still missing. This work thus presents an innovative means to enable a realistic, physical, experimentation of the impacts of novel energy resource management models, without affecting consumers. This is done by using a physical smart city model, which includes several consumers, generation units, and electric vehicles.

Abstract
The deregulation of the electricity sector has culminated in the introduction of competitive markets. In addition, the emergence of new forms of electric energy production, namely the production of renewable energy, has brought additional changes in electricity market operation. Renewable energy has significant advantages, but at the cost of an intermittent character. The generation variability adds new challenges for negotiating players, as they have to deal with a new level of uncertainty. In order to assist players in their decisions, decision support tools enabling assisting players in their negotiations are crucial. Artificial intelligence techniques play an important role in this decision support, as they can provide valuable results in rather small execution times, namely regarding the problem of optimizing the electricity markets participation portfolio. This paper proposes a heuristic method that provides an initial solution that allows metaheuristic techniques to improve their results through a good initialization of the optimization process. Results show that by using the proposed heuristic, multiple metaheuristic optimization methods are able to improve their solutions in a faster execution time, thus providing a valuable contribution for players support in energy markets negotiations.

Abstract
Electricity markets are complex environments with very particular characteristics. Some of the main ones for this complexity are the need for an adequate integration of renewable energy sources and the electricity markets' restructuring process. The growth of simulation tool usage is driven by the need to understand those mechanisms and how the involved players' interactions affect the markets' outcomes. Several modelling tools directed to the study of restructured wholesale electricity markets have emerged. Although, they share a common limitation: the lack of interoperability between the various systems to allow the exchange of information and knowledge, to test different market models and to allow players from different systems to interact in common market environments. This paper proposes the use of ontologies for semantic interoperability between multi-agent platforms in the scope of electricity markets simulation. The achieved results allow the identification of the added value gained by using the proposed ontologies. They facilitate the integration of independent multi-agent simulators, by providing a way for communications to be understood by heterogeneous agents from different systems.

Abstract
Meta-heuristic search methods are used to find near optimal global solutions for difficult optimization problems. These meta-heuristic processes usually require some kind of knowledge to overcome the local optimum locations. One way to achieve diversification is to start the search procedure from a solution already obtained through another method. Since this solution is already validated the algorithm will converge easily to a greater global solution. In this work, several well-known meta-heuristics are used to solve the problem of electricity markets participation portfolio optimization. Their search performance is compared to the performance of a proposed hybrid method (ad-hoc heuristic to generate the initial solution, which is combined with the search method). The addressed problem is the portfolio optimization for energy markets participation, where there are different markets where it is possible to negotiate. In this way the result will be the optimal allocation of electricity in the different markets in order to obtain the maximum return quantified through the objective function.

Abstract
The reduction of the greenhouse gas emissions is a priority all around the globe. The investment on renewable energy sources is contributing for new opportunities in the context of the smart grids and microgrids. Recent advances are transforming the consumer into a prosumer, being able to adapt the consumption depending on its own generated power, and selling the surplus or buying the missing power. In this context, home management systems are emerging as an effective means to support the management of energy resources in the context of communication between functions/devices of a smart home. This paper presents a new agent-based home energy management approach, using ontologies to enable semantic communications between heterogeneous multi-agent entities. The main goal is to support an efficient energy management of end consumers in the context of microgrids, obtaining a scheduling for both real and virtual resources. A case study is presented, which simulates a 25-bus microgrid that includes a laboratorial controlled house (with real and simulated resources), which is managed by the proposed energy management system.

Abstract
Reliable consumption forecasts are crucial in several aspects of power and energy systems, e.g. to take advantage of the full potential of flexibility from consumers and to support the management from operators. With this need, several methodologies for electricity forecasting have emerged. However, the study of correlated external variables, such as temperature or luminosity, is still far from adequate. This paper presents the application of the Wang and Mendel's Fuzzy Rule Learning Method (WM) to forecast electricity consumption. The proposed approach includes two distinct strategies, the first one uses only the electricity consumption as the input of the method, and the second strategy considers a combination of the electricity consumption and the environmental temperature as the input, in order to extract value from the correlation between the two variables. A case study that considers the forecast of the energy consumption of a real office building is also presented. Results show that the WM method using the combination of energy consumption data and environmental temperature is able to provide more reliable forecasts for the energy consumption than several other methods experimented before, namely based on artificial neural networks and support vector machines. Additionally, the WM approach that considers the combination of input values achieves better results than the strategy that considers only the consumption history, hence concluding that WM is appropriate to incorporate different information sources.