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Details

  • Name

    Paulo Antunes Machado
  • Cluster

    Power and Energy
  • Role

    Research Assistant
  • Since

    27th June 2016
001
Publications

2018

Data economy for prosumers in a smart grid ecosystem

Authors
Bessa, RJ; Rua, D; Abreu, C; Machado, P; Andrade, JR; Pinto, R; Gonçalves, C; Reis, M;

Publication
e-Energy 2018 - Proceedings of the 9th ACM International Conference on Future Energy Systems

Abstract
Smart grids technologies are enablers of new business models for domestic consumers with local flexibility (generation, loads, storage) and where access to data is a key requirement in the value stream. However, legislation on personal data privacy and protection imposes the need to develop local models for flexibility modeling and forecasting and exchange models instead of personal data. This paper describes the functional architecture of an home energy management system (HEMS) and its optimization functions. A set of data-driven models, embedded in the HEMS, are discussed for improving renewable energy forecasting skill and modeling multi-period flexibility of distributed energy resources. © 2018 Copyright held by the owner/author(s).

2018

Advanced energy management for demand response and microgeneration integration

Authors
Abreu, C; Rua, D; Machado, P; Lopes, JAP; Heleno, M;

Publication
20th Power Systems Computation Conference, PSCC 2018

Abstract
Energy management is a key tool that will enable consumers to optimize their energy use according to different objectives. Allow users to insert their energy use preferences combined with the effective configuration and control of existing devices (loads and microgeneration) is the basis, in this paper, to design adaptable energy optimization algorithms that are capable of outputting feasible, understandable and useful actions, automated and/or manual, for the activation of the existing portfolio of flexible devices. This paper presents an advanced energy management system as an innovative platform that intends to accomplish real energy optimization schemes to support demand response, promote the energy efficiency and contribute towards renewable integration. © 2018 Power Systems Computation Conference.

2018

Advanced Energy Management for Demand Response and Microgeneration Integration

Authors
Abreu, C; Rua, D; Machado, P; Pecas Lopes, JAP; Heleno, M;

Publication
2018 POWER SYSTEMS COMPUTATION CONFERENCE (PSCC)

Abstract
Energy management is a key tool that will enable consumers to optimize their energy use according to different objectives. Allow users to insert their energy use preferences combined with the effective configuration and control of existing devices (loads and micro generation) is the basis, in this paper, to design adaptable energy optimization algorithms that are capable of outputting feasible, understandable and useful actions, automated and/or manual, for the activation of the existing portfolio of flexible devices. This paper presents an advanced energy management system as an innovative platform that intends to accomplish real energy optimization schemes to support demand response, promote the energy efficiency and contribute towards renewable integration.

2017

AnyPLACE - An Energy Management System to Enhance Demand Response Participation

Authors
Abreu, C; Rua, D; Costa, T; Machado, P; Pecas Lopes, JAP; Heleno, M;

Publication
2017 IEEE MANCHESTER POWERTECH

Abstract
This paper describes an energy management system that is being developed in the AnyPLACE project to support new energy services, like demand response, in residential buildings. In the project end-user interfaces are designed and implemented to allow the input of preferences regarding the flexible use of shiftable and thermal appliances. Monitoring and self-learning algorithm are used to allow additional information to be collected and an automation platform is available for the management and control of appliances. An energy management algorithm is presented that processes end-user preferences and devices characteristics to produce an optimal dispatch considering demand response incentives. Results show the successful implementation of an optimized energy scheduling.