Abstract
Electric power systems have undergone major changes in recent years. Electricity markets are one of the sectors that has been most affected by these changes. Electricity market design is being updated in order to support efficient operation and investments incentives. However, the development of efficient rules is neither easy nor guaranteed. This paper addresses the simulation of multi-participation in electric energy markets. The purpose of this simulation is to offer solutions to electricity market players, in order to support their decisions on future participation situations. For this, artificial intelligence techniques will be used, namely for forecasting and optimization processes. In specific, an optimization approach based on Evolutionary Particle Swarm Optimization (EPSO) is proposed. The achieved results are compared to those of a deterministic resolution method, and of the classical Particle Swarm Optimization (PSO). Results show that the proposed approach is able to achieve higher mean and maximum objective function results than the classical PSO, with a smaller standard deviation. The execution time is higher than using PSO, but still very fast when compared the deterministic method. The case study is based on real data from the Iberian electricity market. © 2018 IEEE.

Abstract
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.

Abstract
The negotiation is one of the most important phase of the process of buying and selling energy in electricity markets. Buyers and sellers know about their own trading behavior or the quality of their products. However, they can also gather data directly or indirectly from them through the exchange information before or during negotiation, even negotiators should also gather information about past behavior of the other parties, such as their trustworthiness and reputation. Hence, in this scope, reputation models play a more important role in decision-making process in the undertaken bilateral negotiation. Since the decision takes into account, not only the potential economic gain for supported player, but also the reliability of the contracts. Therefore, the reputation component represents the level of confidence that the supported player can have on the opponent’s service, i.e. in this case, the level of assurance that the opponent will fulfil the conditions established in the contract. This paper proposes a reputation computational model, included in DECON, a decision support system for bilateral contract negotiation, in order to enhance the decision-making process regarding the choice of the most suitable negotiation parties.

Abstract

Abstract
Modern distributed systems are increasingly complex both on their architectural design and on the computational logic that they execute. Their timely operation is challenged, which is critical for some domains such as cyber-physical systems where timeliness and dynamic behavior must be satisfied simultaneously. Providing real-time operation whereas supporting the inherent dynamic behavior of cyber-physical systems requires solutions that are not yet available. A number of challenging scientific and engineering problems that span across a variety of research areas are raised. The new challenges go far beyond those of traditional networked real-time systems; cyber-physical systems are autonomous, open, large-scale, real-time, embedded, and control systems that make intensive use of networks, distribution, and wireless technology. Such complex systems have different [sub]parts/systems with different levels of real-time requirements.

Abstract
Nowadays, collecting complex information regarding a machine status is the enabler for advanced maintenance activities, and one of the main players in this process is the sensor. This paper describes modern maintenance strategies that lead to Proactive Maintenance (PM), which is the most advanced one. The paper discusses the sensors that can be used to support maintenance, as pertaining to different categories, spanning from common off-the-shelf sensors, to specialized sensors monitoring very specific characteristics, and to virtual sensors. The paper proceeds then to detail three different real world examples of project pilots that make use of the described sensors, and draws a comparison between them. In particular, each scenario has got unique characteristics and prefers different families of sensors, but on the other hand provides similar characteristics on other aspects. In fact, the paper concludes with a discussion regarding how each scenario can benefit from PM and from advanced sensing.