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Two-Stage stochastic optimization for operating a Renewable-Based Microgrid

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  • Abunima, Hamza
  • Park, Woan-Ho
  • Glick, Mark B.
  • Kim, Yun-Su

Abstract

Carbon emissions are increasing as a result of urbanization and population growth all over the world. Scientists agree that these emissions are one of the main causes of climate change and global warming. The power industry is shifting to renewable energy sources (RES) such as solar power (PV) and employing different Energy Storage Systems (ESS) to enable a clean energy future and compensate for the scarcity of fossil fuel. However, the intermittent behaviour of renewable resources causes some obstacles such as power fluctuation, committing extra reserve units, and load shedding. Microgrid (MG) technology is introduced as a promising solution for integrating different RES and loads into the grid. Operating the MG in islanded mode with a limited ESS capacity requires a sophisticated scheduling method. Previous studies on MG addressed the operation issue, neglecting the supply–demand uncertainty or adopting the worst-case scenario. Uncertainty is an inherent characteristic in power systems; on the other hand, considering the worst-case scenario may unnecessarily increase the operation and planning costs. To address the uncertainty and fluctuant characteristics of RES-based MG, this paper proposes a two-stage stochastic optimization integrated with a novel ANN-based prediction model. A new model for PV power prediction is proposed by which the predicted data is in a probability density function (PDF) form. A stochastic optimization (SO) method is proposed to minimize the operation cost and load shedding during the islanding mode. In the proposed SO, the optimal scheduling decision is made in the current moment taking into account the probability of potential supply, load, and ESS capacities in the near future. For this purpose, an ANN-based prediction model is developed to represent the PV output uncertainty in the SO problem. The proposed prediction model proves efficient in the prediction with nRMSE of 9.7% and nMAE of 9.1%. The proposed method is applied to a real Microgrid designed by the Natural Energy Laboratory of Hawaii Authority (NELHA) and compared to a conventional optimization method. The proposed scheduling method reduces both the average Energy Not Supplied (ENS) and operation costs by 19.4% and 18.8%, respectively, with no additional investment cost. A sensitivity study is also conducted to assess the performance of the proposed method in terms of ENS, cost, and simulation time.

Suggested Citation

  • Abunima, Hamza & Park, Woan-Ho & Glick, Mark B. & Kim, Yun-Su, 2022. "Two-Stage stochastic optimization for operating a Renewable-Based Microgrid," Applied Energy, Elsevier, vol. 325(C).
    • Handle: RePEc:eee:appene:v:325:y:2022:i:c:s0306261922011163
    DOI: 10.1016/j.apenergy.2022.119848
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    1. Trigo-González, Mauricio & Batlles, F.J. & Alonso-Montesinos, Joaquín & Ferrada, Pablo & del Sagrado, J. & Martínez-Durbán, M. & Cortés, Marcelo & Portillo, Carlos & Marzo, Aitor, 2019. "Hourly PV production estimation by means of an exportable multiple linear regression model," Renewable Energy, Elsevier, vol. 135(C), pages 303-312.
    2. Xiuyun Wang & Shaoxin Chen & Yibing Zhou & Jian Wang & Yang Cui, 2018. "Optimal Dispatch of Microgrid with Combined Heat and Power System Considering Environmental Cost," Energies, MDPI, vol. 11(10), pages 1-23, September.
    3. Despotovic, Milan & Nedic, Vladimir & Despotovic, Danijela & Cvetanovic, Slobodan, 2016. "Evaluation of empirical models for predicting monthly mean horizontal diffuse solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 246-260.
    4. Matamala, Yolanda & Feijoo, Felipe, 2021. "A two-stage stochastic Stackelberg model for microgrid operation with chance constraints for renewable energy generation uncertainty," Applied Energy, Elsevier, vol. 303(C).
    5. Khan, Ahsan Raza & Mahmood, Anzar & Safdar, Awais & Khan, Zafar A. & Khan, Naveed Ahmed, 2016. "Load forecasting, dynamic pricing and DSM in smart grid: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1311-1322.
    6. Mariam, Lubna & Basu, Malabika & Conlon, Michael F., 2016. "Microgrid: Architecture, policy and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 477-489.
    7. Amir, Vahid & Azimian, Mahdi, 2020. "Dynamic Multi-Carrier Microgrid Deployment Under Uncertainty," Applied Energy, Elsevier, vol. 260(C).
    8. Chen, Tengpeng & Cao, Yuhao & Qing, Xinlin & Zhang, Jingrui & Sun, Yuhao & Amaratunga, Gehan A.J., 2022. "Multi-energy microgrid robust energy management with a novel decision-making strategy," Energy, Elsevier, vol. 239(PA).
    9. Hassan, Muhammed A. & Khalil, A. & Kaseb, S. & Kassem, M.A., 2017. "Exploring the potential of tree-based ensemble methods in solar radiation modeling," Applied Energy, Elsevier, vol. 203(C), pages 897-916.
    10. Han, Dongho & Lee, Jay H., 2021. "Two-stage stochastic programming formulation for optimal design and operation of multi-microgrid system using data-based modeling of renewable energy sources," Applied Energy, Elsevier, vol. 291(C).
    11. Woan-Ho Park & Hamza Abunima & Mark B. Glick & Yun-Su Kim, 2021. "Energy Curtailment Scheduling MILP Formulation for an Islanded Microgrid with High Penetration of Renewable Energy," Energies, MDPI, vol. 14(19), pages 1-15, September.
    12. Roslan, M.F. & Hannan, M.A. & Jern Ker, Pin & Begum, R.A. & Indra Mahlia, TM & Dong, Z.Y., 2021. "Scheduling controller for microgrids energy management system using optimization algorithm in achieving cost saving and emission reduction," Applied Energy, Elsevier, vol. 292(C).
    13. Amith Khandakar & Muhammad E. H. Chowdhury & Monzure- Khoda Kazi & Kamel Benhmed & Farid Touati & Mohammed Al-Hitmi & Antonio Jr S. P. Gonzales, 2019. "Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar," Energies, MDPI, vol. 12(14), pages 1-19, July.
    14. Rae-Kyun Kim & Mark B. Glick & Keith R. Olson & Yun-Su Kim, 2020. "MILP-PSO Combined Optimization Algorithm for an Islanded Microgrid Scheduling with Detailed Battery ESS Efficiency Model and Policy Considerations," Energies, MDPI, vol. 13(8), pages 1-17, April.
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