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Optimal control strategy of battery-integrated energy system considering load demand uncertainty

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  • Sanjari, M.J.
  • Karami, H.

Abstract

Uncertain load behavior affects the energy system operation scheduling in battery-integrated energy systems (BIES) due to the direct relation between optimal battery scheduling and load demand in the forthcoming time interval. In this paper, the optimal schedule of BIES is investigated with considering load demand uncertainty. The previous works have introduced a cost function and found optimal strategies using evolutionary optimization algorithms, which may fall in local minima. Against the previous works and in order to avoid falling in local minima, a closed-form expression is presented in this paper by analyzing a mathematically-modeled forecast error and considering battery characteristics. In the proposed method, the optimal battery charging/discharging state is analytically obtained based on forecast error and system parameters without the need to use numerical approximation algorithms. The applicability of the proposed scheduling strategy in the real energy system is shown by applying it to online optimal control of the battery in a real system with load demand forecast error, and also by comparing the results with previous methods. The proposed approach leads to the globally-optimal solution for battery scheduling and also shrinks the computational complexity of the BIES scheduling problem.

Suggested Citation

  • Sanjari, M.J. & Karami, H., 2020. "Optimal control strategy of battery-integrated energy system considering load demand uncertainty," Energy, Elsevier, vol. 210(C).
    • Handle: RePEc:eee:energy:v:210:y:2020:i:c:s0360544220316339
    DOI: 10.1016/j.energy.2020.118525
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    1. Sharma, Vanika & Haque, Mohammed H. & Aziz, Syed Mahfuzul, 2019. "Energy cost minimization for net zero energy homes through optimal sizing of battery storage system," Renewable Energy, Elsevier, vol. 141(C), pages 278-286.
    2. Wu, Wei & Lin, Boqiang, 2018. "Application value of energy storage in power grid: A special case of China electricity market," Energy, Elsevier, vol. 165(PB), pages 1191-1199.
    3. Nikmehr, Nima & Najafi-Ravadanegh, Sajad & Khodaei, Amin, 2017. "Probabilistic optimal scheduling of networked microgrids considering time-based demand response programs under uncertainty," Applied Energy, Elsevier, vol. 198(C), pages 267-279.
    4. Neves, Sónia Almeida & Marques, António Cardoso & Fuinhas, José Alberto, 2018. "On the drivers of peak electricity demand: What is the role played by battery electric cars?," Energy, Elsevier, vol. 159(C), pages 905-915.
    5. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    6. Taner, Tolga, 2018. "Energy and exergy analyze of PEM fuel cell: A case study of modeling and simulations," Energy, Elsevier, vol. 143(C), pages 284-294.
    7. Yang, Yuqing & Bremner, Stephen & Menictas, Chris & Kay, Merlinde, 2018. "Battery energy storage system size determination in renewable energy systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 109-125.
    8. Rajamand, Sahbasadat, 2020. "Effect of demand response program of loads in cost optimization of microgrid considering uncertain parameters in PV/WT, market price and load demand," Energy, Elsevier, vol. 194(C).
    9. Barman, Mayur & Dev Choudhury, Nalin Behari, 2019. "Season specific approach for short-term load forecasting based on hybrid FA-SVM and similarity concept," Energy, Elsevier, vol. 174(C), pages 886-896.
    Full references (including those not matched with items on IDEAS)

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