DOI:10.1007/s40565-019-0528-3 |
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Optimal microgrid planning for enhancing ancillary service provision |
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Net amount: 988 |
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Author:
Sergio F. CONTRERAS1, Camilo A. CORTES2, Johanna M. A. MYRZIK1
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Author Affiliation:
1. Institute of Automation Technology, University of Bremen, Otto-Hahn-Allee 1 NW1/M1050(70), 28359 Bremen, Germany;
2. Department of Electrical and Electronics Engineering, Universidad Nacional de Colombia, Carrera 30 Calle 45. Ciudad Universitaria Edificio 453, Bogota´, Colombia
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Foundation: |
program ”Becas para Doctorados Nacionales 2014 - 647” of Colciencias as well as the students Andres Felipe Penaranda Bayona and Pablo Elver Mosquera
Duarte from Universidad Nacional de Colombia |
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Abstract: |
Microgrids have presented themselves as an effective concept to guarantee a reliable, efficient and sustainable electricity delivery during the current transition era from passive to active distribution networks. Moreover, microgrids could offer effective ancillary services (AS) to the power utility, although this will not be possible before the traditional planning and operation methodologies are updated. Hence, a probabilistic multi-objective microgrid planning (POMMP) methodology is proposed in this paper to contemplate the large number of variables, multiple objectives, and different constraints and uncertainties involved in the microgrid planning. The planning methodology is based on the optimal size and location of energy distributed resources with the goal of minimizing the mismatch power in islanded mode, while the residual power for AS provision and the investment and operation costs of the microgrid in grid-connected mode are maximized and minimized, respectively. For that purpose, probabilistic models and a true multi-objective optimization problem are implemented in the methodology. The methodology is tested in an adapted PG&E 69-bus distribution system and the non-dominated sorting genetic algorithm II (NSGA-II) optimization method and an analytic hierarchy process for decision-making are used to solve the optimization problem. |
Keywords: |
Microgrid, Ancillary services (AS), Planning, Multi-objective optimization, Decision-making |
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Online Time:2019/07/31 |
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