R. Pereira

Fuzzy clustering applied to a demand response model in a smart grid contingency scenario

This paper focus on a demand response model analysis in a smart grid context considering a contingency scenario. A fuzzy clustering technique is applied on the developed demand response model and an analysis is performed for the contingency scenario. Model considerations and architecture are described. The demand response developed model aims to support consumers decisions regarding their consumption needs and possible economic benefits.

Demand Response Analysis in Smart Grids Using Fuzzy Clustering Model

This paper focuses on an analysis of demand response in a smart grid context, presenting the model considerations and architecture. Domestic consumption is divided into groups in order to cover the adequate modeling. A fuzzy subtractive clustering method is applied to demand response on several domestic consumption scenarios and results analyses are presented. The demand response developed model aims to support consumers decisions regarding their consumption needs and possible economical benefits.

Analysis of Domestic Prosumer Influence on a Smartgrid

The shift from a centralized production to a distributed generation based on renewable energy production has been increasing, resulting in prosumer arising as new stakeholder of the electrical system. In this paper, a prosumer that owns photovoltaic solar panels and small wind generator, is considered and renewable energy production forecast is given by artificial neural networks (ANN). An energy management model is developed considering a battery storage system. Several case studies are analyzed considering seasonality factors.

Residence Efficiency Based on Smart Energy Systems

The resources used to maintain all the activities in buildings are limited and, for this reason, it is critical the usage of energy efficiency measures. This paper is focused on a smart controller development able to automatically manage a building with the objective of improving its energy efficiency. The controller uses Artificial Neural Networks (ANN) as a forecast method and Fuzzy Logic Control (FLC) as a decision maker. The thermal house model is simulated using a MATLAB toolbox. One of the sub controllers is developed in this paper and is responsible for the HVAC control and optimization. The developed system uses renewable energy sources attached to an energy storage system to increase the overall energy efficiency. To demonstrate the effectiveness of the developed controller it is made an economic analysis of its influence in the build system.

Computational Models Development and Demand Response Application for Smart Grids

This paper focuses on computational models development and its applications on demand response, within smart grid scope. A prosumer model is presented and the corresponding economic dispatch problem solution is analyzed. The prosumer solar radiation production and energy consumption are forecasted by artificial neural networks. The existing demand response models are studied and a computational tool based on fuzzy clustering algorithm is developed and the results discussed. Consumer energy management applications within the InovGrid pilot project are presented. Computation systems are developed for the acquisition, monitoring, control and supervision of consumption data provided by smart meters, allowing the incorporation of consumer actions on their electrical energy management. An energy management system with integration of smart meters for energy consumers in a smart grid is developed.

Integration of Evora-InovGrid smartmeters in a consumer's SCADA system

This paper develops an energy management system with smart meters integration for electricity consumers in a smart grid context. The integration of a distributor owned smart meter from the main Portuguese electricity distributor - EDP - is developed. The smart meter is connected to a common PC, that runs the Matlab Software and communicates to the SCADA system (Supervisory Control And Data Acquisition) using the OPC Protocol. The SCADA system supervises an industrial network of Programmable Logic Controllers (PLC). The developed control strategy implements a hierarchical cascade controller where inner loops are performed by local PLCs and the outer loop is managed by a centralized SCADA system, which interacts with the entire local PLC network.