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A novel Coral Reefs Optimization algorithm with substrate layers for optimal battery scheduling optimization in micro-grids

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Abstract

In this paper we propose a Coral Reefs Optimization algorithm with substrate layers (CRO-SL) to tackle the battery scheduling optimization problem in micro-grids (MGs). Specifically, we consider a MG that includes renewable generation and different loads, defined by their power profiles, and is equipped with an energy storage device (battery) to address its scheduling (charge/discharge duration and occurrence) in a real scenario of variable electricity prices. The CRO-SL is a recently proposed meta-heuristic which promotes co-evolution of different exploration models within a unique population. We fully describe the proposed CRO-SL algorithm, including its initialization and the different operators implemented in the algorithm. Experiments in a real MG scenario are carried out. To show the good battery scheduling performance of the proposed CRO-SL, we have compared the results with what we called a deterministic procedure. The deterministic charge/discharge approach is defined as a fixed way of using the energy storage device that only depends on the pattern of the loads and generation profiles considered. Hourly values of both generation and consumption profiles have been considered, and the good performance of the proposed CRO-SL is shown for four different weeks of the year (one per season), where the effect of the battery scheduling optimization obtains savings up 10 % of the total electricity cost in the MG, when compared with the deterministic procedure.

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References

  • Al-Saedi W, Lachowicz SW, Habibi D, Bass O (2013) Power flow control in grid-connected microgrid operation using Particle Swarm Optimization under variable load conditions. Int J Electr Power Energy Syst 49:76–85

    Article  Google Scholar 

  • Asmus P (2010) Microgrids, virtual power plants and our distributed energy future. Electr J 23(10):72–82

    Article  Google Scholar 

  • Bajpai P, Dash V (2012) Hybrid renewable energy systems for power generation in stand-alone applications: a review. Renew Sustain Energy Rev 16:2926–2939

    Article  Google Scholar 

  • Berry A, Platt G, Cornforth D (2010) Minigrids: analyzing the state-of-play. In: Proceedings of the IEEE international power electronics conference, pp 710–716

  • “BOE-A-2014-1052,” (2014) Spanish official bulletin, no. 28, pp 7147–7697

  • Devi S, Geethanjali M (2014) Application of Modified Bacterial foraging Optimization algorithm for optimal placement and sizing of Distributed Generation. Expert Syst Appl 41:2772–2781

    Article  Google Scholar 

  • Doagou-Mojarrad H, Gharehpetian GB, Rastegar H, Olamaei J (2013) Optimal placement and sizing of DG (distributed generation) units in distribution networks by novel hybrid evolutionary algorithm. Energy 54:129–138

    Article  Google Scholar 

  • Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Springer, New York

  • Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68

    Article  Google Scholar 

  • Ghosh S, Ghoshal SP, Ghosh S (2010) Optimal sizing and placement of distributed generation in a network system. Int J Electr Power Energy Syst 32:849–856

    Article  Google Scholar 

  • Gözeland T, Hocaoglu MH (2009) An analytical method for the sizing and siting of distributed generators in radial systems. Electr Power Syst Res 79:912–918

    Article  Google Scholar 

  • Haesen E, Espinoza M, Pluymers B, Goethals I, Thong VV et al (2005) Optimal placement and sizing of distributed generator units using genetic optimization algorithms. Elect Power Qual Util J XI:97–104

    Google Scholar 

  • Jiayi H, Chuanwen J, Rong X (2008) A review of distributed energy resources and MicroGrid. Renew Sustain Energy Rev 12:2472–2483

    Article  Google Scholar 

  • Kirpatrick D, Gerlatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680

    Article  MathSciNet  Google Scholar 

  • Kusakana K (2015) Operation cost minimization of photovoltaic-diesel-battery hybrid systems. Energy 85:645–653

    Article  Google Scholar 

  • Li M, Miao C, Leung C (2015) A Coral Reef algorithm based on learning automata for the coverage control problem of Heterogeneous directional sensor networks. Sensors 15:30617–30635

    Article  Google Scholar 

  • Mallol-Poyato R, Salcedo-Sanz S, Jiménez-Fernández S, Díaz-Villar P (2015) Optimal discharge scheduling of energy storage systems in MicroGrids based on hyper-heuristics. Renew Energy 83:13–24

    Article  Google Scholar 

  • Mallol-Poyato R, Jiménez-Fernández S, Díaz-Villar P, Salcedo-Sanz S (2016) Joint optimization of a Microgrid’s structure design and its operation using a two-steps evolutionary algorithm. Energy 94(1):775–785

  • Medeiros IG, Xavier-Júnior JC, Canuto AM (2015) Applying the Coral Reefs Optimization algorithm to clustering problems. In: Proceedings of the international joint conference on neural networks (IJCNN), pp 1–8

  • Moghaddam AA, Seifi A, Niknam T, Pahlavani MR (2011) Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel/battery hybrid power source. Energy 36:6490–6507

    Article  Google Scholar 

  • Moradi MH, Abedini M (2012) A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems. Int J Electr Power Energy Syst 34:66–74

    Article  Google Scholar 

  • Planas E, Gil de Muro A, Andreu J, Kortabarria I, Martínez de Alegría I (2013) General aspects, hierarchical controls and droop methods in microgrids: a review. Renew Sustain Energy Rev 17:147–159

    Article  Google Scholar 

  • Price KV, Storn R (1997) Differential evolution: a simple evolution strategy for fast optimization. Dr. Dobb’s J 22(4):18–24

    MathSciNet  MATH  Google Scholar 

  • Real Decreto 1164/2001, October 26th, that establishes access tariffs for electric energy transport and distribution. https://www.boe.es/boe/dias/2001/11/08/pdfs/A40618-40629

  • “Reference power demand and consumption profiles,” Red Eléctrica de Espana, http://www.ree.es/es/actividades/operacion-del-sistema/, last accessed 05/05/2016

  • Salcedo-Sanz S, Gallo-Marazuela D, Pastor-Sánchez A, Carro-Calvo L, Portilla-Figueras JA, Prieto L (2014a) Offshore wind farm design with the Coral Reefs Optimization algorithm. Renew Energy 63:109–115

  • Salcedo-Sanz S, Casanova-Mateo C, Pastor-Sánchez A, Sánchez-Girón M (2014b) Daily global solar radiation prediction based on a hybrid Coral Reefs Optimization—extreme learning machine approach. Sol Energy 105:91–98

  • Salcedo-Sanz S, Del Ser J, Landa-Torres I, Gil-López S, Portilla-Figueras JA (2014c) The Coral Reefs Optimization algorithm: a novel metaheuristic for efficiently solving optimization problems. Sci World J 2014, Article ID: 739768

  • Salcedo-Sanz S, Pastor-Sánchez A, Del Ser J, Prieto L, Geem ZW (2015) A Coral Reefs Optimization algorithm with Harmony Search operators for accurate wind speed prediction. Renew Energy 75:93–101

    Article  Google Scholar 

  • Salcedo-Sanz S, Camacho-Gómez C, Molina, D, Herrera F (2016a) A Coral Reefs Optimization algorithm with substrate layers and local search for large scale global optimization. In: Proceedings of the IEEE conference on evolutionary algorithms, Vancouver, Canada, pp 1–8

  • Salcedo-Sanz S, Muñoz-Bulnes J, Vermeij M (2016b) New Coral Reefs-based approaches for the model type selection problem a novel method to predict a nation’s future energy demand. Int J Bio Inspired Comput (in press)

  • Severini M, Squartini S, Piazza F (2013) Hybrid soft computing algorithmic framework for smart home energy management. Soft Comput 17(11):1983–2005

  • Taher SA, Hasani M, Karimian A (2011) A novel method for optimal capacitor placement and sizing in distribution systems with nonlinear loads and DG using GA. Commun Nonlinear Sci Numer Simul 16:851–862

    Article  Google Scholar 

  • Tan X, Li Q, Wang H (2013) Advances and trends of energy storage technology in Microgrid. Int J Electr Power Energy Syst 44(1):179–191

    Article  Google Scholar 

  • “Tariff 2.0 prices. BOE-A-2013-13803” (2013) Spanish official bulletin, no. 313, pp 106840–107066

  • “Tariff 3.1. prices,” Endesa Energía. http://www.endesaonline.com/es/empresas/. Last accessed 06 May 2016

  • Velik RM (2013) The influence of battery storage size on photovoltaics energy self-consumption for grid-connected residential buildings. Int J Adv Renew Energy Res 2(6):1–7

    Google Scholar 

  • Vermeij MJ (2005) Substrate composition and adult distribution determine recruitment patterns in a Caribbean brooding coral. Mar Ecol Prog Ser 295:123–133

    Article  Google Scholar 

  • Xu FY, Zhou L, Lai LL (2010) Application of artificial neural network in electrical analysis of micro-grid load. In: Proceedings of the IEEE power and energy society general meeting, pp 1–5

  • Yang Y, Zhang S, Xiao Y (2015) Optimal design of distributed energy resource systems coupled with energy distribution networks. Energy 85:433–448

    Article  Google Scholar 

  • Yang Z, Zhang T, Zhang D (2016) A novel algorithm with differential evolution and coral reef optimization for extreme learning machine training. Cogn Neurodynamics 10(1):73–83

  • Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102

    Article  Google Scholar 

  • Zeng Z, Yang H, Zhao R, Cheng C (2013) Topologies and control strategies of multi-functional grid-connected inverters for power quality enhancement: A comprehensive review. Renew Sustain Energy Rev 24:223–270

    Article  Google Scholar 

  • Zhao B, Zhang X, Li P, Wang K, Xue M, Wang C (2014) Optimal sizing, operating strategy and operational experience of a stand-alone microgrid on Dongfushan Island. Appl Energy 113:1656–1666

    Article  Google Scholar 

  • Zhao J, Yuan X (2016) Multi-objective optimization of stand-alone hybrid PV-wind-diesel-battery system using improved fruit fly optimization algorithm. Soft Comput 20(7):2841–2853

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Authors and Affiliations

  1. Department of Signal Processing and Communications, Universidad de Alcalá, Madrid, Spain

    S. Salcedo-Sanz, C. Camacho-Gómez, R. Mallol-Poyato & S. Jiménez-Fernández

  2. Optima Group, TECNALIA, 48160 , Derio, Bizkaia, Spain

    J. Del Ser

  3. Department of Communications Engineering, University of the Basque Country EHU/UPV, 48013, Bilbao, Bizkaia, Spain

    J. Del Ser

Authors
  1. S. Salcedo-Sanz
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  2. C. Camacho-Gómez
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  3. R. Mallol-Poyato
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  4. S. Jiménez-Fernández
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  5. J. Del Ser
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Corresponding author

Correspondence to S. Salcedo-Sanz.

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Funding

This work has been partially funding by the Spanish Ministerial Commission of Science and Technology, MICYT, Grant Number: TIN2014-54583-C2-2-R and Comunidad de Madrid, Grant Number: S2013ICE-2933_02.

Conflict of interest

All the authors declare that they have no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Communicated by A. Herrero.

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Salcedo-Sanz, S., Camacho-Gómez, C., Mallol-Poyato, R. et al. A novel Coral Reefs Optimization algorithm with substrate layers for optimal battery scheduling optimization in micro-grids. Soft Comput 20, 4287–4300 (2016). https://doi.org/10.1007/s00500-016-2295-7

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  • DOI: https://doi.org/10.1007/s00500-016-2295-7

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