A Clusterized Energy Management with Linearized Losses in the Presence of Multiple Types of Distributed Generation
| Autor: | Remy Rigo-Mariani, Jan M. Maciejowski, Keck Voon Ling |
|---|---|
| Přispěvatelé: | Laboratoire de Génie Electrique de Grenoble (G2ELab ), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Nanyang Technological University [Singapour], Department of Engineering [Cambridge], University of Cambridge [UK] (CAM), School of Electrical and Electronic Engineering, Energy Research Institute @ NTU (ERI@N) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Mathematical optimization
Energy management Computer science 020209 energy Energy Engineering and Power Technology Distributed Generation 02 engineering and technology network partitioning computer.software_genre CO2 emissions 7. Clean energy Energy storage law.invention Linearization law Systems management 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Cluster analysis MILP business.industry energy storage 020208 electrical & electronic engineering [SPI.NRJ]Engineering Sciences [physics]/Electric power Electricity generation Distributed generation Electrical network Electrical and electronic engineering [Engineering] controllable load business computer |
| Zdroj: | International Journal of Electrical Power and Energy Systems International Journal of Electrical Power and Energy Systems, Elsevier, 2019, 113, pp.9-22 HAL |
| ISSN: | 0142-0615 |
| Popis: | This paper presents an optimal management (OM) strategy for distributed generation (DG) planning studies. The objective is the reduction of the CO2 emissions for the power generation on Jurong Island in Singapore. Different DG resources are investigated with solar panels, energy storage units, small gas turbines as well as controllable loads in addition to the centralized generation already in site. Each of those resources is modeled in an optimal scheduling procedure that furtherly allows to test several DG configurations (i.e. different types/sizes/sites) with regards to the CO2 emissions. The paper mainly focuses on the implementation of the OM and the main challenge is to avoid prohibitive computational times, which is tackled thanks to two approaches. At first, a linearization of the line losses with a modified DC power flow is considered while optimizing the system management over a representative day. A generic clustering method is then developed along with a sequential optimal management (S-OM) lying on both nodal and zonal representations of the electrical network. Different validation tests are performed as well as sets of simulations with several DG configurations. The optimal DG planning procedure itself is not in the scope of that paper and will be part of further developments. National Research Foundation (NRF) Accepted version The authors acknowledge the support by the Singapore National Research Foundation under its Campus for Research Excellence And Technological Enterprise (CREATE) programme and the Cambridge Centre for Advanced Research in Energy Efficiency in Singapore Ltd (CARES). |
| Databáze: | OpenAIRE |
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