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Scheduling coupled photovoltaic, battery and conventional energy sources to maximize profit using linear programming

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  • Torres, David
  • Crichigno, Jorge
  • Padilla, Gregg
  • Rivera, Ruben

Abstract

To address the increase of electricity demand, the need for reducing carbon dioxide, and the reduction of available fossil fuel resources, renewable energy sources are being recruited. Specifically energy generated by photovoltaic (PV) cells is becoming one of the most promising alternatives. In this context, this paper presents an optimization model for the scheduling problem where conventional and photovoltaic sources of energy are scheduled to be delivered to satisfy energy demand. The optimization model is formulated as a Linear Program (LP) with a bounded number of variables and constraints. The respective solution can be obtained in polynomial time and provides the optimal combination or schedule of energy generated from different sources (conventional, renewable and battery storage) such that the total demand is satisfied and the profit is maximized. Numerical results demonstrate the effectiveness and the generality of the scheme.

Suggested Citation

  • Torres, David & Crichigno, Jorge & Padilla, Gregg & Rivera, Ruben, 2014. "Scheduling coupled photovoltaic, battery and conventional energy sources to maximize profit using linear programming," Renewable Energy, Elsevier, vol. 72(C), pages 284-290.
    • Handle: RePEc:eee:renene:v:72:y:2014:i:c:p:284-290
    DOI: 10.1016/j.renene.2014.07.006
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    References listed on IDEAS

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    1. Nottrott, A. & Kleissl, J. & Washom, B., 2013. "Energy dispatch schedule optimization and cost benefit analysis for grid-connected, photovoltaic-battery storage systems," Renewable Energy, Elsevier, vol. 55(C), pages 230-240.
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    6. Nge, Chee Lim & Ranaweera, Iromi U. & Midtgård, Ole-Morten & Norum, Lars, 2019. "A real-time energy management system for smart grid integrated photovoltaic generation with battery storage," Renewable Energy, Elsevier, vol. 130(C), pages 774-785.
    7. Deng, Na & Cai, Rongchang & Gao, Yuan & Zhou, Zhihua & He, Guansong & Liu, Dongyi & Zhang, Awen, 2017. "A MINLP model of optimal scheduling for a district heating and cooling system: A case study of an energy station in Tianjin," Energy, Elsevier, vol. 141(C), pages 1750-1763.
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    9. Mohammed Qais & K. H. Loo & Hany M. Hasanien & Saad Alghuwainem, 2023. "Optimal Comfortable Load Schedule for Home Energy Management Including Photovoltaic and Battery Systems," Sustainability, MDPI, vol. 15(12), pages 1-15, June.
    10. Steffen Limmer & Nils Einecke, 2022. "An Efficient Approach for Peak-Load-Aware Scheduling of Energy-Intensive Tasks in the Context of a Public IEEE Challenge," Energies, MDPI, vol. 15(10), pages 1-23, May.
    11. Dominik McInnis & Massimiliano Capezzali, 2020. "Managing Wind Turbine Generators with a Profit Maximized Approach," Sustainability, MDPI, vol. 12(17), pages 1-16, September.
    12. Beibei Wang & Xiaocong Liu & Feng Zhu & Xiaoqing Hu & Wenlu Ji & Shengchun Yang & Ke Wang & Shuhai Feng, 2015. "Unit Commitment Model Considering Flexible Scheduling of Demand Response for High Wind Integration," Energies, MDPI, vol. 8(12), pages 1-22, December.

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