Abstract
The continuous changes in electricity markets' mechanisms and operations turn this environment into a challenging domain for the participating entities. Simulation tools are increasingly being used for decision support purposes of such entities. In particular, multi-agent based simulation, which facilitates the modeling of different types of mechanisms and players, is being fruitfully applied to the study of worldwide electricity markets. An effective decision support to market players' negotiations is, however, still not properly reached due to the uncertainty that results from the increasing penetration of renewable generation and the complexity of market mechanisms themselves. In this scope, this paper proposes a novel metalearner that provides decision support to market players in their negotiations. The proposed metalearner uses as input the output of several other market negotiation strategies, which are used to create a new, enhanced response. The final result is achieved through the combination and evolution of the strategies' learning results by applying a genetic algorithm.

Abstract
Power systems have been through deep changes, with their operation in the scope of competitive electricity markets (EM) and the increasingly intensive use of renewable energy sources and distributed generation. This requires new business models able to cope with the new opportunities. Virtual Power Players (VPPs) are a new player type which allows aggregating a diversity of players (distribution Generation, storage units, electrical vehicles, and consumers) to participate in the markets and to provide a set of new services promoting generation and consumption efficiency and to improving players’ benefits. A major task of VPPs is the remuneration of generation and of the services (e.g. market operation costs, and energy reserves) as well as charging energy consumption. This PhD research will contribute by developing fair and strategic remuneration and tariff methodologies, able to allow efficient VPP operation and VPP goals accomplishment in EM. © Springer International Publishing AG 2018.

Abstract
The energy sector has suffered a significant restructuring that has increased the complexity in electricity market players' interactions. The complexity that these changes brought requires the creation of decision support tools to facilitate the study and understanding of these markets. The Multiagent Simulator of Competitive Electricity Markets (MASCEM) arose in this context, providing a simulation framework for deregulated electricity markets. The Adaptive Learning strategic Bidding System (ALBidS) is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM, ALBidS considers several different strategic methodologies based on highly distinct approaches. Six Thinking Hats (STH) is a powerful technique used to look at decisions from different perspectives, forcing the thinker to move outside its usual way of thinking. This paper aims to complement the ALBidS strategies by combining them and taking advantage of their different perspectives through the use of the STH group decision technique. The combination of ALBidS' strategies is performed through the application of a genetic algorithm, resulting in an evolutionary learning approach.

Abstract
The increase of distributed energy resources, mainly based on renewable sources, requires new solutions that are able to deal with this type of resources' particular characteristics (namely, the renewable energy sources intermittent nature). The smart grid concept is increasing its consensus as the most suitable solution to facilitate the small players' participation in electric power negotiations while improving energy efficiency. The opportunity for players' participation in multiple energy negotiation environments (smart grid negotiation in addition to the already implemented market types, such as day-ahead spot markets, balancing markets, intraday negotiations, bilateral contracts, forward and futures negotiations, and among other) requires players to take suitable decisions on whether to, and how to participate in each market type. This paper proposes a portfolio optimization methodology, which provides the best investment profile for a market player, considering different market opportunities. The amount of power that each supported player should negotiate in each available market type in order to maximize its profits, considers the prices that are expected to be achieved in each market, in different contexts. The price forecasts are performed using artificial neural networks, providing a specific database with the expected prices in the different market types, at each time. This database is then used as input by an evolutionary particle swarm optimization process, which originates the most advantage participation portfolio for the market player. The proposed approach is tested and validated with simulations performed in multiagent simulator of competitive electricity markets, using real electricity markets data from the Iberian operator-MIBEL.

Abstract
Several challenges arrive with electrical power restructuring, liberalized electricity markets emerge, aiming to improve the system's efficiency while offering new economic solutions. Privatization and liberalization of previously nationally owned systems are examples of the transformations that have been applied. Microgrids and smart grids emerge and new business models able to cope with new opportunities start being developed. New types of players appear, allowing aggregating a diversity of entities, e. g. generation, storage, electric vehicles, and consumers, Virtual Power Players (VPPs) are a new type of player that allows aggregating a diversity of players to facilitate their participation in the electricity markets. A major task of VPPs is the remuneration of generation and services (maintenance, market operation costs and energy reserves), as well as charging energy consumption. The paper proposes a normalization method that supports a clustering methodology for the remuneration and tariffs definition. This model uses a clustering algorithm, applied on normalized load values, the value of the micro production, generated in the bus associated to the same load, was subtracted from the value of the consumption of that load. This calculation is performed in a real smart grid on buses with associated micro production. This allows the creation of sub-groups of data according to their correlations. The clustering process is evaluated so that the number of data sub-groups that brings the most added value for the decision making process is found, according to players characteristics.

Abstract
In recent years the reassessment of remuneration schemes for renewable sources in several European countries has motivated the increase of wind power generation participation in electricity markets. Moreover, the continuous growth of wind power generation, as well as the evolution of wind turbines technology, suggests that wind power plants may participate in both energy and ancillary services markets with strategic behavior to improve their benefits. Thus, wind power generation with strategic behavior may have impact on market equilibrium and pricing. This paper evaluates the impact of a proportional offering strategy for wind power plants to participate in both energy and ancillary services markets. MASCEM (Multi-Agent System for Competitive Electricity Markets) is used to simulate and validate the impact of wind power plants in market equilibrium. A case study based on real and recent data for the Iberian market and its specific rules is simulated in MASCEM. © 2015 IEEE.