Abstract
The price evolution in electricity market with large share of renewables often exhibits a deep volatility, triggered by external factors such as wind and water availability, load level and also by business strategies of market agents. Consequently, in many real applications, the performance of electricity price is not appropriate. The goal of this article is to analyze the available market data and characterize circumstances that affect the evolution of prices, in order to allow the identification of states that promote price instability and to confirm that class segmentation allows increasing forecast performance. A regression technique (based on Artificial Neural Networks) was applied first to the whole set and then to each class individually. Performances results showed a clear advantage (above 20%) of the second approach when compared to the first one.

Abstract
In a consumer setting, storage systems can be dispatched in order to shift surplus generation to periods when a local generation deficit exists. However, the high investment cost still makes the deployment of storage unattractive. As a way to overcome this problem existing literature looking at storage installed at the grid-level suggests dispatching the storage device for multiple applications simultaneously in order to access several value streams. Therefore, in this work, a Mixed Integer Linear Program is developed in order to schedule the operation of a storage device in a consumer context for multiple objectives in parallel. Besides shifting locally generated energy in time, the peak demand seen by the electric grid is reduced and the storage device is operated to provide primary reserve control. The model is applied in a case study based on the current German situation in order to illustrate the value contribution of stacking multiple services. When pursuing multiple applications simultaneously, the revenues of storage can be increased significantly. However, the revenues are not additive due to conflicting operations which originates a revenue gap as illustrated in the paper.

Abstract
The unbundling of the electricity sector in several activities, some of them provided in a regulated way and some others under competition, poses a number of challenging problems namely because in several areas there are conflicting objectives associated to different stakeholders. These different views and objectives paved the way to the development of new multiobjective tools able to represent this new paradigm. In this scope, this paper presents a multiobjective (MO) formulation for the Transmission Expansion Planning (TEP) problem using a new solution approach that combines concepts of evolutionary computation and multi agent population algorithms. The new proposed tool is termed as Multi-Population and Multiobjective Evolutionary Particle Swarm Optimization - MEPSO-II. The TEP problem is handled in a realistic way preserving the holistic view over the entire planning horizon and the true grid behavior because it considers the multi-stage nature of the problem and we use an AC Optimal Power Flow (AC-OPF) model to gain insight on the operation conditions of the network. The multi objective formulation considers the total system cost, on one side, and the Expected Power Not Supplied (EPNS), on the other. The total system cost comprises the investment cost in new equipment and the operation costs while the EPNS takes into account the uncertainties related to the non- ideal behavior of system components using a non-chronological Monte Carlo simulation. Numerical simulations are conducted using the IEEE 24 and the 118 Bus Test Systems in order to compare the proposed MO tool against other algorithms through performance evaluation indices. Although being a higher time-consuming tool, the MEPSO-II enables improving the Pareto-Front and therefore it gives more insight to transmission network planners when compared with other consolidated algorithms described in the literature.

Abstract
Over the last few years, electrical storage and especially battery systems have seen a strong rise in interest. In several countries, as for instance in Germany, lithium-ion batteries are now commonly deployed in end-consumer installations to shift local generation from photovoltaic systems in time. A further application for storage is price arbitrage, which corresponds to an operation strategy benefitting from price differentials. In this work, we describe a Mixed Integer Problem to optimize the storage dispatch considering both the 15- and the 60-min auctions in use in Germany. Furthermore, in addition to the calendric lifetime, the limitation to a certain number of cycles is considered in the evaluation. Last, it was conducted a sensitivity analysis to identify the price volatility level that is required to generate a profit from arbitrage operations. Therefore, a market price process with adjustable parameters has been implemented.

Abstract
Electric vehicles will certainly play an important and increasing role in the transport sector over the next years. As their number grows, they will affect the behavior of the electricity demand seen not only by distribution but also by transmission networks and so changes will also occur in the operation and expansion planning of the power systems. In this sense, this paper addresses the impact of large fleets of Plug-in-Electric Vehicles (PEVs) in transmission equipment investments. The developed model uses evolutionary particle swarm optimization (EPSO) to handle the planning problem over different scenarios regarding the evolution of PEVs and their impact on the demand. These scenarios consider the PEVs penetration level, the availability of charging and the related charging policies. The paper includes a Case Study based on the IEEE 24 busbar power system model for a 10-year period. The model uses an AC Optimal Power Flow to analyse the operation of the system for different investment paths over the years and the results show that coordinating the charging of PEVs can be an interesting solution to postpone the investments in transmission equipment thus reducing the associated costs.

Abstract
The Brazilian electricity market is characterized by having around 65% of its installed capacity coming from hydropower plants, with multiple agents coexisting in the same hydro cascades. Currently, it also contains certain peculiarities that distinguish it from other markets, such as the Energy Reallocation Mechanism (MRE), a centerpiece of the Brazilian market's design. This paper proposes replacing the MRE with a bid-based short-term market called the virtual reservoir model. To simulate the behavior of the hydros in this new market, an agent-based model is implemented using the reinforcement Q-learning algorithm, simulated annealing and linear programming. In the simulations, we use real data - encompassing more than 98% of the total hydro installed capacity and three years of market data - from the Brazilian power system. The results indicate that the management of (virtual) reservoirs can be the responsibility of each hydro: these can save water according to their own risk perceptions, while maintaining current efficiency and security levels. The results also suggest that the final monthly short-term market prices can substantially decrease in comparison with the current prices.