Adriano Ferreira

Integration of an agent-based strategic planner in an enterprise service bus ecosystem

The continuous change in the manufacturing world is demanding more flexible, responsive and accurate planning tools, which are able to assist the decision-makers to take tactical and strategic decisions on short notice with a high level of confidence. For this purpose, these tools should dynamically explore different operative scenarios in the planning procedure and produce information about key performance indicators. This paper describes the development of an agent-based strategic planner, combining the flexibility of multi-agent systems principles with the optimization capability of a Mixed Integral Programming technique. The tool is integrated in an ecosystem of heterogeneous decision-making systems through an Enterprise Service Bus that also provides access to legacy data.

Increasing self-sustainability in micro grids using load prioritization and forecasting mechanisms

Self-sustainability of micro grids is an important challenge to be addressed, particularly when they operate disconnected from the utility grid. The multi-agent systems paradigm allows implementing intelligent and predictive strategies towards an efficient management of the power flow. In this paper, an agent-based model is introduced, optimizing the load demand, through load prioritization, by shedding and rescheduling, and using forecasting mechanisms to improve the micro grids self-sustainability. The designed agent-based model and strategies were implemented and tested in the Repast agent-based modelling and simulation framework. Experimental results show improvements in the self-sustainability of the micro grid under analysis when operating in critical mode.

Genetic algorithm for flexible job shop scheduling problem - A case study

This paper proposes the impact assessment of the workers in the optimal time of operations in a Flexible Job Shop Scheduling Problem. In this work, a real enterprise was studied. The problem consists in finding the workers operations schedule, taking into account the precedence constraints. The main objective is to minimize the finish time of the last task completed in the schedule. The genetic algorithm was used to solve the optimization problem and some numerical results are presented.

Managing intelligent self-sustained electrical micro-grids

Micro grids play an important role in the context of smart grids, improving the system reliability, resilience and stability. In case of micro grids disconnected from the main grid, due to geographical reasons or failure in the service provider, the self-sustainability management is a key issue. This paper discusses agent-based automation and control approaches to handle the self-sustainability in electrical stand-alone micro grids. The proposed agent-based model is applied to a case study and simulated using the Netlogo agent-based modelling platform, allowing to analyze different control and prediction strategies to improve their self-sustainability.

Challenges of ICT and artificial intelligence in smart grids

This paper provides an overview of the actual developments on smart grids, particularly analysing the efforts on research and development in the different regions in the world. The paper also discusses the roles of Artificial Intelligence (AI) and Information & Communication Technologies (ICT) in the future development of smart grids. Particularly, it highlights the importance of power simulation tools in developing control strategies and discusses how AI techniques can work with these tools to provide simulation solutions that are closer to a real implementation.

Formal Specification of a Self-sustainable Holonic System for Smart Electrical Micro-grids

Stand-alone micro-grids have emerged within the smart grids field, facing important challenges related to their proper and efficient operation. An example is the self-sustainability when the micro-grid is disconnected from the main utility, e.g., due to a failure in the main utility or due to geographical situations, which requires the efficient control of energy demand and production. This paper describes the formal specification of a holonic system architecture that is able to perform the automation control functions in electrical stand-alone micro-grids, particularly aiming to improve their self-sustainability. The system aims at optimizing the power flow among the different electrical players, both producers and consumers, to keep the micro-grid operating even under adverse situations. The behaviour of each individual holon and their coordination patterns were modelled, analysed and validated using the Petri net formalism, allowing the complete verification of the system correctness during the design phase.

Prediction Models for Short-Term Load and Production Forecasting in Smart Electrical Grids

The scheduling of household smart load devices play a key role in microgrid ecosystems, and particularly in underpowered grids. The management and sustainability of these microgrids could benefit from the application of short-term prediction for the energy production and demand, which have been successfully applied and matured in larger scale systems, namely national power grids. However, the dynamic change of energy demand, due to the necessary adjustments aiming to render the microgrid self-sustainability, makes the forecasting process harder. This paper analyses some prediction techniques to be embedded in intelligent and distributed agents responsible to manage electrical microgrids, and especially increase their self-sustainability. These prediction techniques are implemented in R language and compared according to different prediction and historical data horizons. The experimental results shows that none is the optimal solution for all criteria, but allow to identify the best prediction techniques for each scenario and time scope.

Holonic self-sustainable systems for electrical micro grids

The self-sustainability of micro grids is an important challenge in the smart grids field due to the need to balance the fast growing energy consumption, reduce greenhouse gas emissions and increase energy independence by using renewable resources. The use of decentralized paradigms, and particularly multi-agent systems and holonic control, enable to face this challenge by implementing intelligent mechanisms that allow an efficient management of the power flow. In this paper, a holonic based model is introduced, considering load scheduling and forecast mechanisms to improve the micro grids self-sustainability, and consequently reduce the energy cost and the energy dependency from the main utility. The designed holonic based model and strategies were developed by using the agent technology, and particularly the JADE framework, showing important improvements in the self-sustainability of micro grids working in different operating modes.

Preliminary results on Alfvén wave system in the TCABR tokamak

A brief review of the Alfven Wave Excitation System (AWES) designed for the TCABR tokamak and the first experimental results on RF plasma heating are presented. One of four antenna modules has been completely installed in the vacuum chamber and the initial experiments were carried out in the low power regime using the four-phase RF generator. The main objectives were the antenna tuning according to the typical plasma parameters of TCABR and the evaluation of the antenna parasitic loading, as well as the calibration of the RF diagnostic tools in real discharge conditions. The first results have been obtained with standard diagnostics and with the RF signals measured using high sampling rate digital oscilloscopes. They showed that daily antenna cleaning and correct wave helicity excitation reduce significantly the parasitic loading and are crucial for efficient plasma coupling.