Adapting Meeting Tools to Agent Decision

Electricity 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.

Stochastic Framework for Strategic Decision-making of Load-serving Entities for Day-ahead Market

The deregulation of electricity markets has diversified the range of financial transaction modes between independent system operator (ISO), generation companies (GENCO) and load-serving entities (LSE) as the main interacting players of a day-ahead market (DAM). LSEs sell electricity to end-users and retail customers. The LSE that owns distributed generation (DG) or energy storage units can supply part of its serving loads when the nodal price of electricity rises. This opportunity stimulates them to have storage or generation facilities at the buses with higher locational marginal prices (LMP). The short-term advantage of this model is reducing the risk of financial losses for LSEs in DAMs and its long-term benefit for the LSEs and the whole system is market power mitigation by virtually increasing the price elasticity of demand. This model also enables the LSEs to manage the financial risks with a stochastic programming framework.

Virtual Power Players Internal Negotiation and Management in MASCEM

Electricity Markets are not only a new reality but an evolving one as the involved players and rules change at a relatively high rate. Multi-agent simulation combined with Artificial Intelligence techniques may result in very helpful sophisticated tools. This paper presents a new methodology for the management of coalitions in electricity markets. This approach is tested using the multi-agent market simulator MASCEM (Multi-Agent Simulator of Competitive Electricity Markets), taking advantage of its ability to provide the means to model and simulate Virtual Power Players (VPP). VPPs are represented as coalitions of agents, with the capability of negotiating both in the market and internally, with their members in order to combine and manage their individual specific characteristics and goals, with the strategy and objectives of the VPP itself. A case study using real data from the Iberian Electricity Market is performed to validate and illustrate the proposed approach.

Decision support tool for Virtual Power Players: Hybrid Particle Swarm Optimization applied to Day-ahead Vehicle-To-Grid Scheduling

This paper presents a decision support tool methodology to help virtual power players (VPPs) in the Smart Grid (SGs) context to solve the day-ahead energy resource scheduling considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G). The main focus is the application of a new hybrid method combing a particle swarm approach and a deterministic technique based on mixedinteger linear programming (MILP) to solve the day-ahead scheduling minimizing total operation costs from the aggregator point of view. A realistic mathematical formulation, considering the electric network constraints and V2G charging and discharging efficiencies is presented. Full AC power flow calculation is included in the hybrid method to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Upper Ontology for Multi-Agent Energy Systems’ Applications

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors research group has developed three multi-agent systems: MASCEM, which simulates the electricity markets; ALBidS that works as a decision support system for market players; and MASGriP, which simulates the internal operations of smart grids. To take better advantage of these systems, their integration is mandatory. For this reason, is proposed the development of an upper-ontology which allows an easier cooperation and adequate communication between them. Additionally, the concepts and rules defined by this ontology can be expanded and complemented by the needs of other simulation and real systems in the same areas as the mentioned systems. Each system’s particular ontology must be extended from this top-level ontology.

Reinforcement Learning Based on the Bayesian Theorem for Electricity Markets Decision Support

This paper presents the applicability of a reinforcement learning algorithm based on the application of the Bayesian theorem of probability. The proposed reinforcement learning algorithm is an advantageous and indispensable tool for ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to electricity market negotiating players. ALBidS uses a set of different strategies for providing decision support to market players. These strategies are used accordingly to their probability of success for each different context. The approach proposed in this paper uses a Bayesian network for deciding the most probably successful action at each time, depending on past events. The performance of the proposed methodology is tested using electricity market simulations in MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). MASCEM provides the means for simulating a real electricity market environment, based on real data from real electricity market operators.

Decision Support for Energy Contracts Negotiation with Game Theory and Adaptive Learning

This paper presents a decision support methodology for electricity market players’ bilateral contract negotiations. The proposed model is based on the application of game theory, using artificial intelligence to enhance decision support method’s adaptive features. This model is integrated in AiD-EM (Adaptive Decision Support for Electricity Markets Negotiations), a multi-agent system that provides electricity market players with strategic behavior capabilities to improve their outcomes from energy contracts’ negotiations. Although a diversity of tools that enable the study and simulation of electricity markets has emerged during the past few years, these are mostly directed to the analysis of market models and power systems’ technical constraints, making them suitable tools to support decisions of market operators and regulators. However, the equally important support of market negotiating players’ decisions is being highly neglected. The proposed model contributes to overcome the existing gap concerning effective and realistic decision support for electricity market negotiating entities. The proposed method is validated by realistic electricity market simulations using real data from the Iberian market operator—MIBEL. Results show that the proposed adaptive decision support features enable electricity market players to improve their outcomes from bilateral contracts’ negotiations.

Six thinking hats: A novel metalearner for intelligent decision support in electricity markets

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.

Metodologia BIM, aplicada à preparação, controlo e gestão de obra

O âmbito deste trabalho envolve o teste do modelo BIM numa obra em construção pela Mota-Engil – Engenharia, na extração experimental de peças desenhadas de preparação e apoio à execução de obra. No capítulo 1 deste relatório são definidos o âmbito e os objetivos deste trabalho, é feito um enquadramento histórico do tema e abordados conceitos e atividades da preparação de obra, na sua forma tradicional. O estado do conhecimento da preparação de obras e mais em concreto da tecnologia BIM a nível nacional e internacional é abordado no capítulo 2. Nesse sentido procura-se definir os conceitos principais inerentes a esta nova metodologia, que passa por identificar e caraterizar a tecnologia envolvida e o seu nível de desenvolvimento. Com suporte em casos práticos de preparação de obra na sua forma tradicional, identificados e desenvolvidos no capítulo 3, foi compilado um processo tipo de peças desenhadas de suporte identificadas e caracterizadas no capítulo 4, frequentes e comuns à execução de diversos tipos de obras de edifícios. Assente na compilação baseada em casos práticos e no estudo do projeto de execução da empreitada que sustenta o presente trabalho, com base no qual o modelo BIM foi concebido, identificou-se um conjunto de peças desenhadas de preparação e apoio à execução dos trabalhos, em 2D, a extrair do modelo. No capítulo 5, é feita uma descrição do modo como foi estudado o projeto da obra, com evidência para os fatores mais relevantes, especificando os desenhos a extrair. Suportada pelo programa de modelação ArchiCAD, a extração do conjunto de desenhos identificados anteriormente foi conseguida com recurso às funcionalidades disponíveis no software, que permite a criação de desenhos 2D atualizáveis ou não automaticamente a partir do modelo. Qualquer alteração introduzida no modelo virtual é automaticamente atualizada nos desenhos bidimensionais, caso o utilizador assim o pretenda. Ao longo desse trabalho foram detetados e analisados os condicionalismos inerentes ao processo de extração, referidos no capítulo 6, para estabelecimento de regras de modelação padrão a adotar em futuras empreitadas, que possam simplificar a obtenção dos elementos desenhados de preparação necessários à sua execução. No ponto 6.3 são identificadas melhorias a introduzir no modelo. Em conclusão no capítulo 7 são abordadas especificidades do setor da construção que sustentam e evidenciam cada vez mais a necessidade de utilizar as novas tecnologias com vista à adoção de práticas e ferramentas padrão de apoio à execução de obras. Sendo a tecnologia BIM, transversal a todo o setor, a sua utilização com regras padrão na conceção dos modelos e na extração de dados, potencia a otimização dos custos, do tempo, dos recursos e da qualidade final de um empreendimento, ao longo de todo o seu ciclo de vida, para além de apoiar com elevada fiabilidade as tomadas de decisão ao longo desse período. A tecnologia BIM, possibilita a antevisão do edifício a construir com um elevado grau de pormenor, com todas as vantagens que daí advêm.