Filipe Fernandes

Intelligent SCADA for Load control

A supervisory control and data acquisition (SCADA) system is an integrated platform that incorporates several components and it has been applied in the field of power systems and several engineering applications to monitor, operate and control a lot of processes.

Contextual and environmental awareness laboratory for energy consumption management

The recent changes on power systems paradigm requires the active participation of small and medium players in energy management. With an electricity price fluctuation these players must manage the consumption. Lowering costs and ensuring adequate user comfort levels. Demand response can improve the power system management and bring benefits for the small and medium players. The work presented in this paper, which is developed aiming the smart grid context, can also be used in the current power system paradigm. The proposed system is the combination of several fields of research, namely multi-agent systems and artificial neural networks. This system is physically implemented in our laboratories and it is used daily by researchers. The physical implementation gives the system an improvement in the proof of concept, distancing itself from the conventional systems. This paper presents a case study illustrating the simulation of real-time pricing in a laboratory.

Management of Heating, Ventilation and Air Conditioning system for SHIM platform

Several approaches have been proposed according the concepts of smart grids and the smart home is one of them. A smart home can be defined as a system with network communication between all devices allowing the control, monitoring and remote access of the management system, progressing in a generalized way of all loads for an individual way, through individual management of loads. The paper proposes a Heating, Ventilation and Air Conditioning (HVAC) management methodology to be included in the Supervisor Control and Data Acquisition House Intelligent Management (SHIM). SHIM is a simulation platform developed and implemented in the Knowledge Engineering and Decision Support Research Center (GECAD) to support the control and management of appliances of end consumers. The main goal of the presented work is to develop a HVAC management methodology consisting in a priority system that classifies the importance of the HVAC in each instant. The priority classification depends directly on the difference between the room temperature and the user desired temperature, in order to take measures to optimize consumption during events with power consumption limitations maintaining user comfort.

Dynamic Approach and Testbed for Small and Medium Players Simulation in Smart Grid Environments

Abstract The Smart Grid environment allows the integration of resources of small and medium players through the use of Demand Response programs. Despite the clear advantages for the grid, the integration of consumers must be carefully done. This paper proposes a system which simulates small and medium players. The system is essential to produce tests and studies about the active participation of small and medium players in the Smart Grid environment. When comparing to similar systems, the advantages comprise the capability to deal with three types of loads – virtual, contextual and real. It can have several loads optimization modules and it can run in real time. The use of modules and the dynamic configuration of the player results in a system which can represent different players in an easy and independent way. This paper describes the system and all its capabilities.

Multi-Agent based Smart Grid management and simulation: Situation awareness and learning in a test bed with simulated and real installations and players

The rising usage of distributed energy resources has been creating several problems in power systems operation. Virtual Power Players arise as a solution for the management of such resources. Additionally, approaching the main network as a series of subsystems gives birth to the concepts of smart grid and micro grid. Simulation, particularly based on multi-agent technology is suitable to model all these new and evolving concepts. MASGriP (Multi-Agent Smart Grid simulation Platform) is a system that was developed to allow deep studies of the mentioned concepts. This paper focuses on a laboratorial test bed which represents a house managed by a MASGriP player. This player is able to control a real installation, responding to requests sent by the system operators and reacting to observed events depending on the context.

SCADA house intelligent management for energy efficiency analysis in domestic consumers

The implementation of smart homes allows the domestic consumer to be an active player in the context of the Smart Grid (SG). This paper presents an intelligent house management system that is being developed by the authors to manage, in real time, the power consumption, the micro generation system, the charge and discharge of the electric or plug-in hybrid vehicles, and the participation in Demand Response (DR) programs. The paper proposes a method for the energy efficiency analysis of a domestic consumer using the SCADA House Intelligent Management (SHIM) system. The main goal of the present paper is to demonstrate the economic benefits of the implemented method. The case study considers the consumption data of some real cases of Portuguese house consumption over 30 days of June of 2012, the Portuguese real energy price, the implementation of the power limits at different times of the day and the economic benefits analysis.

Dynamic loads and micro-generation method for a House Management System

Several approaches have been proposed in the context of the smart grids and microgrids to validate the active participation of the end consumers in the energy management process. The present paper focuses in a model with dynamic application of optimization algorithm to manage the active participation of the end consumers in the demand response events. The method is performed using real and simulated loads and real micro-generation data from Polytechnic of Porto and from University of São Paulo. The methodology includes the operation requirements for each resource, namely loads, micro-generation and electrical grid. A case study with two scenarios is presented bearing in mind the difference between resources profiles of two countries. The results of dynamic management model in domestic consumers are discussed for the participation in a 2 hours demand response event with same time characteristics. In both scenarios, the power limit depends on the loads consumption and micro-generation.

Hour-ahead energy resource management in university campus microgrid

The management of intermittent energy resources by a microgrid operator (MGO) are a key aspect of future smart grids. A microgrid allows act as a single controllable entity and can operate in both grid-connected or in islanded mode. The present paper proposes a platform to be used by MGO regarding the energy resource management in smart grids and considering day-ahead and hour-ahead time-horizons. This method considers that the energy resources are managed by a MGO that establishes contracts with their client. The operator is able to manage the consumption and generation resources, also including demand response programs, obtaining profits from selling electricity to the main network. The model uses full AC power flow and the energy buying and selling from/to the main network. The case study considers the internal low voltage distribution network of a real university campus, and the projection of distributed energy resources based on the evolution scenario for the year 2050.

Smart meters as a tool for energy efficiency

Gradually smart grids and smart meters are closer to the home consumers. Several countries has developed studies focused in the impacts arising from the introduction of these technologies and one of the main advantages are related to energy efficiency, observed through the awareness of the population on behalf of a more efficient consumption. These benefits are felt directly by consumers through the savings on electricity bills and also by the concessionaires through the minimization of losses in transmission and distribution, system stability, smaller loading during peak hours, among others. In this article two projects that demonstrate the potential energy savings through smart meters and smart grids are presented. The first performed in Korea, focusing on the installation of smart meters and the impact of use of user interfaces. The second performed in Portugal, focusing on the control of loads in a residence with distributed generation.

ANN-based LMP forecasting in a distribution network with large penetration of DG

In recent years, power systems have experienced many changes in their paradigm. The introduction of new players in the management of distributed generation leads to the decentralization of control and decision-making, so that each player is able to play in the market environment. In the new context, it will be very relevant that aggregator players allow midsize, small and micro players to act in a competitive environment. In order to achieve their objectives, virtual power players and single players are required to optimize their energy resource management process. To achieve this, it is essential to have financial resources capable of providing access to appropriate decision support tools. As small players have difficulties in having access to such tools, it is necessary that these players can benefit from alternative methodologies to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), and intended to support smaller players. In this case the present methodology uses a training set that is created using energy resource scheduling solutions obtained using a mixed-integer linear programming (MIP) approach as the reference optimization methodology. The trained network is used to obtain locational marginal prices in a distribution network. The main goal of the paper is to verify the accuracy of the ANN based approach. Moreover, the use of a single ANN is compared with the use of two or more ANN to forecast the locational marginal price.