Fuel Consumption Reduction and Energy Management in Stand-Alone Hybrid Microgrid under Load Uncertainty and Demand Response by Linear Programming

Document Type : Research paper

Authors

1 Department of Electrical Engineering, University of Mohaghegh Ardabili, Ardabil, Iran

2 Ardabil Province Electricity Distribution Co.

Abstract

A stand-alone microgrid usually contains a set of distributed generation resources, energy storage system and loads that can be used to supply electricity of remote areas. These areas are small in terms of population and industry. Connection of these areas to the national distribution network due to the high costs of constructing transmission lines is not economical. Optimal utilization and economic management of production units and storage devices are important issues in isolated microgrids. During optimum utilization, of renewable energy harvesting is maximized and fuel cost of diesel units reduces as much as possible. In this paper, the optimization problem is designed and solved as Linear Programming (LP). The cost of diesel generator unit depends on its production. Also, the fact is considered that the efficiency of diesel generator units is not constant for all amount of production. As a solution for this challenge demand side management plans have been proposed. On the other hand, load uncertainty is considered in this paper. Several scenarios are simulated by GAMS software for different conditions of a typical microgrid. The simulation results show the success of the proposed method in reducing costs and fossil fuel consumption and increasing the consumption of renewable energy.

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References

[1]    The World Bank, “Designing sustainable off-grid rural electrification projects, principles and practices,” Washington, DC, USA, 2008.
[2]    S.G. Banerjee et al., “Global tracking framework,” Sustain Energy All World Bank, vol.3, pp. 289, 2013.
[3]    United Nations, “Affordable and clean energy: energy efficient,” New York, NY, USA, 2018.
[4]    The World Bank, “State of energy access report 2017”, Washington, DC, USA, 2017.
[5]    IRENA, “Off-grid renewable energy solutions: global and regional status and trends,” Abu Dhabi, UAE, pp. 1-20, 2018.
[6]    S. Perry, J. Klemeš and I. Bulatov, “Integrating waste and renewable energy to reduce the carbon footprint of locally integrated energy sectors,” Energy, vol.33, pp. 1489-1497, 2008.
[7]    J.Y.Lee, K.B. Aviso and R.R. Tan, “Optimal sizing and design of hybrid power systems,” ACS Sustain. Chem. Eng., vol.6, pp. 2482-2490, 2018.
[8]    J.Y. Lee, Y.C. Lu, K.B. Aviso and R.R. Tan, “Mathematical programming for optimal design of hybrid power systems with uncertainties,” Chem. Eng. Trans., vol. 70, pp. 67-72, 2018.
[9]    Z. Geem, “Optimal scheduling of multiple dam system using harmony search algorithm,” Comput. Ambient Intell., vol. 4507, pp. 316-323, 2007.
[10]  A. Ray, K. Jana and S. De, “Polygeneration for an off-grid Indian village: Optimization by economic and reliability analysis,” Appl. Therm. Eng., vol. 116, pp. 182–196, 2017.
[11]  K. Khalilpour and A. Vassallo, “A generic framework for distributed multi-generation and multi-storage energy systems,” Energy, vol. 114, pp. 798-813, 2016.
[12]  H. Zimmermann, “Fuzzy programming and linear programming with several objective functions,” Fuzzy Sets Syst., vol. 1, pp. 45-55, 1978.
[13]  A. Ubando et al.,” systematic approach for the optimal design of an off-grid polygeneration system using fuzzy linear programming model,” Comput. Aided Chem. Eng., vol. 40, pp. 2191-2196, 2017.
[14]  A. Ubando, I. Marfori, K.  Aviso and R.  Tan, “Optimal synthesis of a community-based off-grid polygeneration plant using fuzzy mixed integer linear programming model,” Chem. Eng. Trans., vol. 70, pp. 955-960, 2018.
[15]  H. Kasivisvanathan, I. Barilea, D. Ng and R.  Tan, “Optimal operational adjustment in multi-functional energy systems in response to process inoperability,” Appl. Energy, vol.102, pp. 492-500, 2013.
[16]   R. Tan, C. Cayamanda and K. Aviso, “P-graph approach to optimal operational adjustment inpoly-generation plants under conditions of process inoperability,” Appl. Energy, vol. 135, pp. 402-406, 2014.
[17]  G. Chiu, K. Aviso, A. Ubando and R. Tan, “Fuzzy linear programming model for the optimal design of a combined cooling, heating, and power plant,” Proc. IEEE 9th Int. Conf. Humanoid Nanotechnol. Inf. Technol. Commun. Control Environ. Manage. Philippines, 2017.
[18]  A. Mayol et al., “Fuzzy linear programming model for the optimal design of a trigeneration plant with product price variability,” Proc. IEEE Region 10 Annu. Int. Conf., Singapore, 2017.
[19]  V. Jayadev and K. Swarup, “optimization of microgrid with demand side management using genetic algorithm,” IET Conf. Power Unity: a Whole Syst. Approach, United Kingdom, 2013.
[20]  A. Dolara, F. Grimaccia, G. Magistrati and G. Marchegiani, “Optimization models for islanded micro-grids: a comparative analysis between linear program-ming and mixed integer programming,” Energies, vol. 10, pp. 241, 2017.
[21]  M. Alilou, D. Nazarpour and H. Shayeghi, “Multi-objective optimization of demand side management and multi dg in the distribution system with demand response,” J. Oper. Autom. Power Eng., vol. 6, pp. 230-242, 2018
[22]  E. Shahryari, H. Shayeghi, B. Mohammadi and M. Morad Zadeh, “Optimal energy management of micro-grid in day-ahead and intra-day markets using a copula-based uncertainty modeling method,” J. Oper. Autom. Power Eng., In press, 2020.
Journal of Operation and Automation in Power Engineering
Volume 8, Issue 3
December 2020
Pages 273-281

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History

  • Receive Date: 31 August 2019
  • Revise Date: 30 October 2019
  • Accept Date: 04 January 2020

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