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
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 use of Decision Support Systems (DSS) in the field of Electricity Markets (EM) is essential to provide strategic support to its players. EM are constantly changing, dynamic environments, with many entities which give them a particularly complex nature. There are several simulators for this purpose, including Bilateral Contracting. However, a gap is noticeable in the pre-negotiation phase of energy transactions, particularly in gathering information on opposing negotiators. This paper presents an overview of existing tools for decision support to the Bilateral Contracting in EM, and proposes a new tool that addresses the identified gap, using concepts related to automated negotiation, game theory and data mining.

Abstract

Abstract
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 Copyright © 2017 for this paper by its authors.