Abstract
With the advent of restructuring, generation companies have to plan the operation of their stations in order to maximize their profits. This is very relevant for companies having a large share of hydro stations, and even more if these stations have pumping capacity. This paper describes a model to plan the operation of a set of hydro stations eventually installed in cascade and admitting that some of them are pumping stations. Once a first set of operation orders is obtained using a Genetic Algorithm, their generation/load values are included in the expected market selling/buying curves and the hourly prices are updated. These prices are then used to refine the operation orders originating an iterative process so that hydro stations are price makers. The paper includes results for a hydro system in order to illustrate the application of the developed approach.

Abstract
This paper discusses the current Brazilian electricity market, brings out some dilemmas that should be examined in order to implement a more market-oriented approach, and describes a new market design to overcome these issues. The proposed market design is based on virtual reservoirs and aims at enhancing the flexibility to enable market participants to comply with their contracts, while still ensuring the efficient use of the water and maintaining the current level of the security of supply. In addition, to simulate the behavior of the market participants in this new framework, an Agent-Based Model ABM where agents use reinforcement Q-Learning - is developed and applied to a case study that includes a centralized dispatch as currently exists in Brazil. The results obtained so far show that this new design is suitable to allow hydros managing their commercial contract commitments with extra flexibility.

Abstract
In order to overcome a number of difficulties detected on the Brazilian electricity market this paper proposes and tests a bid based short-term market. To simulate the behavior of the hydros in this new market, it was implemented an Agent-Based Model - ABM using the reinforcement QLearning algorithm, Simulated Annealing, and linear programming. In the simulations we used real data from the Brazilian power system encompassing more than 98% of the total hydro installed capacity and three years of market data. The results indicate that the management of (virtual) reservoirs can be under the responsibility of each hydro, which could save water according to their own risk perception, while it is maintained the current efficiency and security levels. Results also suggest that the final monthly short-term market prices can substantially decrease in comparison with the current prices.

Abstract
The emergence of electricity markets and the opportunities consequently created have radically modified the operation planning activities of Power Systems. As direct result, there is the need for developing new methods to meet in an efficient way the requirements of generation companies, so to respond adequately to new challenges such as market competition. Agent-based models have been proposed in the literature as surrogate to equilibrium models when the problem is too complex to be addressed within a traditional equilibrium framework. Driven by this trend, this paper discusses and proposes a conceptual model following the agent paradigm that deals with the inherent complexity of electricity markets. The devised model proved to be robust enough to represent key features of our study domain and is ready to be actually developed in a computational tool to help generation companies to build their short-medium term operation decisions in a more sounded and robust way.

Abstract
The optimization and simulation of power systems continues to be an area of concern for electricity companies and researchers worldwide namely considering the development of electricity markets and competition in the generation activity Therefore generation companies are devoting an increasing attention to market issues justifying the development of models to help them preparing bidding strategies to the day-ahead market. In this context, agent-based models have been reported as a complement to optimization and equilibrium models when the problem is too complex to be analyzed by traditional approaches. This paper details an Agent-Based Model for an electricity market considering a detailed modeling for hydro stations and presents some preliminary results taking the Iberian Electricity Market as an example.

Abstract
In the last decades power systems witnessed the implementation of an organizational and operational restructuring that lead to the introduction of competitive mechanisms in some activities of the value chain. This is the case of generation and retailing with the development of wholesale and retail markets. These developments together with a renewed emphasis on the adoption of more sustainable solutions while maintaining adequate security of supply levels contributed to increase the interest of generation companies for models enabling the optimization of the use of generation assets or for models and tools to help them to prepare and test bidding strategies to the day-ahead markets. Having in mind the increased complexity of the operation of power systems, Agent-Based Models, ABM, are been used to complement the traditional optimization and equilibrium models, taking advantage of the interaction between agents acting in a simulation environment. In this scope, this paper describes an ABM model that uses Q-learning to provide knowledge for the agents to behave in an optimal way. This model is designed to mimic the main features of the common electricity market between Portugal and Spain, the MIBEL. Apart from describing the developed model, this paper also includes preliminary results from its application to the MIBEL case.