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Optimal design of Microgrid’s network topology and location of the distributed renewable energy resources using the Harmony Search algorithm

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Abstract

In this paper, we tackle the joint optimization of the network topology and the optimal location of distributed renewable energy resources in a Microgrid (MG). The MG network topology optimization problem is focused on obtaining network deployments with minimal cost, whereas the location of distributed renewable generation is associated with the minimization of the electricity losses in the MG lines. In order to solve this joint optimization problem, we analyze the efficiency of the Harmony Search (HS), a novel meta-heuristic solver inspired by the music improvisation procedure observed in jazz bands. We consider two different approaches, the first one is a single-objective formulation of the problem, where the classical HS is applied with some adaptations. The second approach is to consider a multi-objective version of the HS algorithm, able to evolve a whole family of solutions in a Pareto front. Both approaches have been tested on two small-sized MGs: an 8 node MG and a 12 node MG, and results have been compared to an 8 node and a 12 node baseline scenario, respectively, obtaining improvements of up to 42%.

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Notes

  1. “Reglamento Electrotécnico para Baja Tensión. ITC-07. Redes Subterráneas de Distribución en Baja Tensión,” Spanish Ministry of Science and Technology (Ministerio de Ciencia y Tecnología), http://www.f2i2.net/documentos/lsi/rbt/ITC_BT_07.pdf, 2002. Last access: (April 2018).

Abbreviations

\(a_{ik}\) :

Cross-sectional area of the line that connects nodes i and k

C :

MG’s lines total cost

\(C_\mathrm{ins}\) :

Power line’s installation unity cost per m

\(C_\mathrm{mat}\) :

Material’s unity cost per m mm\(^2\)

\(\mathcal {E}_{ik}\) :

Energy losses in the line that connects nodes i and k

E :

MG’s energy losses

\(\mathcal {G}_i\) :

Power generation at node i

\(\mathbb {G}\) :

Total number of generators in the MG

\(\mathbb {G}^P\) :

Number of photovoltaic generators in the MG

\(\mathbb {G}^W\) :

Number of micro-wind turbines in the MG

\(\mathcal {L}_i\) :

Loads’ consumption at node i

\(\mathscr {L}_{ik}\) :

Power losses in the line that connects nodes i and k

\(l_{ik}\) :

Length of the line that connects nodes i and k

\(\mathcal {M}\) :

MG under design

MG:

Microgrid

N :

Number of nodes in the MG considered

PCC:

Point of Common Coupling

\(P_{ik}\) :

Power transmitted over the line that connects nodes i and k

\(P_i\) :

Active power at node i

\(Q_i\) :

Reactive power at node i

\(\rho \) :

Material resistivity

\(R_{ik}\) :

Resistance of the line that connects nodes i and k

\(\varvec{ \mathcal {T}}\) :

MG’s topology matrix

U (Volts):

MG nominal voltage value

V (p.u.):

MG nominal voltage value

\(Y_{ik}\) :

Admittance of the line that connects nodes i and k

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Acknowledgements

This work has been partially supported by the project TIN2014-54583-C2-2-R of the Spanish Ministerial Commission of Science and Technology (MICYT), by the Comunidad Autónoma de Madrid, under project number S2013ICE-2933_02, and by the Basque Government, under ELKARTEK program (BID3ABI project).

Funding

This study was partially funded by the Spanish Ministerial Commission of Science and Technology (MICYT project number TIN2014-54583-C2-2-R), by the Comunidad Autónoma de Madrid (project number S2013ICE-2933_02), and by the Basque Government (BID3ABI project).

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Camacho-Gómez, C., Jiménez-Fernández, S., Mallol-Poyato, R. et al. Optimal design of Microgrid’s network topology and location of the distributed renewable energy resources using the Harmony Search algorithm. Soft Comput 23, 6495–6510 (2019). https://doi.org/10.1007/s00500-018-3300-0

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