Browsing by Author "Vale, Zita"
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- 2017 IEEE competition on modern heuristic optimizers for smart grid operation: Testbeds and resultsPublication . Lezama, Fernando; Soares, João; Vale, Zita; Rueda, Jose; Rivera, Sergio; Elrich, IstvánThis paper summarizes the two testbeds, datasets, and results of the IEEE PES Working Group on Modern Heuristic Optimization (WGMHO) 2017 Competition on Smart Grid Operation Problems. The competition is organized with the aim of closing the gap between theory and real-world applications of evolutionary computation. Testbed 1 considers stochastic OPF (Optimal Power Flow) based Active-Reactive Power Dispatch (ARPD) under uncertainty and Testbed 2 large-scale optimal scheduling of distributed energy resources. Classical optimization methods are not able to deal with the proposed optimization problems within a reasonable time, often requiring more than one day to provide the optimal solution and a significant amount of memory to perform the computation. The proposed problems can be addressed using modern heuristic optimization approaches, enabling the achievement of good solutions in much lower execution times, adequate for the envisaged decision-making processes. Results from the competition show that metaheuristics can be successfully applied in search of efficient near-optimal solutions for the Stochastic Optimal Power Flow and large-scale energy resource management problems.
- Adaptable control for electrical generation at irregural wind speedsPublication . Puga, Ricardo; Ferreira, Judite; Cunha, J. Boaventura; Vale, ZitaThe main aims of this work are the development and the validation of one generic algorithm to provide the optimal control of small power wind generators. That means up to 40 kW and blades with fixed pitch angle. This algorithm allows the development of controllers to fetch the wind generators at the desired operational point in variable operating conditions. The problems posed by the variable wind intensity are solved using the proposed algorithm. This is done with no explicit measure of the wind velocity, and so no special equipment or anemometer is required to compute or measure the wind velocity.
- Adapting Meeting Tools to Agent DecisionPublication . Barreto, João; Praça, Isabel; Pinto, Tiago; Sousa, Tiago; Vale, ZitaElectricity markets are complex environments comprising several negotiation mechanisms. MASCEM (Multi- Agent System for Competitive Electricity Markets) is a simulator developed to allow deep studies of the interactions between the players that take part in the electricity market negotiations. ALBidS (Adaptive Learning Strategic Bidding System) is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM it considers several different methodologies based on very distinct approaches. The Six Thinking Hats is a powerful technique used to look at decisions from different perspectives. This paper aims to complement ALBidS strategies usage by MASCEM players, providing, through the Six Thinking Hats group decision technique, a means to combine them and take advantages from their different perspectives. The combination of the different proposals resulting from ALBidS’ strategies is performed through the application of a Genetic Algorithm, resulting in an evolutionary learning approach.
- Adaptive learning in agents behaviour: A framework for electricity markets simulationPublication . Pinto, Tiago; Vale, Zita; Sousa, Tiago; Praça, Isabel; Santos, Gabriel; Morais, HugoElectricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.
- Adaptive Learning in Games: Defining Profiles of Competitor PlayersPublication . Pinto, Tiago; Vale, ZitaArtificial Intelligence has been applied to dynamic games for many years. The ultimate goal is creating responses in virtual entities that display human-like reasoning in the definition of their behaviors. However, virtual entities that can be mistaken for real persons are yet very far from being fully achieved. This paper presents an adaptive learning based methodology for the definition of players’ profiles, with the purpose of supporting decisions of virtual entities. The proposed methodology is based on reinforcement learning algorithms, which are responsible for choosing, along the time, with the gathering of experience, the most appropriate from a set of different learning approaches. These learning approaches have very distinct natures, from mathematical to artificial intelligence and data analysis methodologies, so that the methodology is prepared for very distinct situations. This way it is equipped with a variety of tools that individually can be useful for each encountered situation. The proposed methodology is tested firstly on two simpler computer versus human player games: the rock-paper-scissors game, and a penalty-shootout simulation. Finally, the methodology is applied to the definition of action profiles of electricity market players; players that compete in a dynamic game-wise environment, in which the main goal is the achievement of the highest possible profits in the market.
- Adaptive learning in multiagent systems for automated energy contacts negotiationPublication . Pinto, Tiago; Vale, ZitaThis paper presents the Adaptive Decision Support for Electricity Markets Negotiations (AiD-EM) system. AiD-EM is a multi-agent system that provides decision support to market players by incorporating multiple sub-(agent-based) systems, directed to the decision support of specific problems. These sub-systems make use of different artificial intelligence methodologies, such as machine learning and evolutionary computation, to enable players adaptation in the planning phase and in actual negotiations in auction-based markets and bilateral negotiations.
- Adaptive learning in multiagent systems: a forecasting methodology based on error analysisPublication . Sousa, Tiago; Pinto, Tiago; Vale, Zita; Praça, Isabel; Morais, H.Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simu-lator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM pro-vides several dynamic strategies for agents’ behaviour. This paper presents a method that aims to provide market players strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses an auxiliary forecasting tool, e.g. an Artificial Neural Net-work, to predict the electricity market prices, and analyses its forecasting error patterns. Through the recognition of such patterns occurrence, the method predicts the expected error for the next forecast, and uses it to adapt the actual forecast. The goal is to approximate the forecast to the real value, reducing the forecasting error.
- Adaptive Portfolio Optimization for Multiple Electricity Markets ParticipationPublication . Pinto, Tiago; Morais, Hugo; Sousa, Tiago M.; Sousa, Tiago; Vale, Zita; Praça, Isabel; Faia, Ricardo; Pires, Eduardo José SolteiroThe 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.
- Adaptive tool for automatic data collection of real electricity marketsPublication . Praça, Isabel; Sousa, Tiago; Freitas, A.; Pinto, Tiago; Vale, Zita; Silva, MarcoThe study of electricity markets operation has been gaining an increasing importance in last years, as result of the new challenges that the electricity markets restructuring produced. This restructuring increased the competitiveness of the market, but with it its complexity. The growing complexity and unpredictability of the market’s evolution consequently increases the decision making difficulty. Therefore, the intervenient entities are forced to rethink their behaviour and market strategies. Currently, lots of information concerning electricity markets is available. These data, concerning innumerous regards of electricity markets operation, is accessible free of charge, and it is essential for understanding and suitably modelling electricity markets. This paper proposes a tool which is able to handle, store and dynamically update data. The development of the proposed tool is expected to be of great importance to improve the comprehension of electricity markets and the interactions among the involved entities.
- Adjacent Markets Influence Over Electricity Trading—Iberian Benchmark StudyPublication . Morais, Hugo; Pinto, Tiago; Vale, ZitaThis paper presents a study on the impact of adjacent markets on the electricity market, realizing the advantages of acting in several different markets. The increased use of renewable primary sources to generate electricity and new usages of electricity such as electric mobility are contributing to a better and more rational way of living. The investment in renewable technologies for the distributed generation has been creating new opportunities for owners of such technologies. Besides the selling of electricity and related services (ancillary services) in energy markets, players can participate and negotiate in other markets, such as the carbon/CO2 market, the guarantees of origin market, or provide district heating services selling of steam and hot water among others. These market mechanisms are related to the energy market, originating a wide market strategy improving the benefits of using distributed generators. This paper describes several adjacent markets and how do they complement the electricity market. The paper also shows how the simulation of electricity and adjacent markets can be performed, using an electricity market simulator, and demonstrates, based on market simulations using real data from the Iberian market, that the participation in various complementary markets can enable power producers to obtain extra profits that are essential to cover the production costs and facilities maintenance. The findings of this paper enhance the advantages for investment on energy production based renewable sources and more efficient technologies of energy conversion