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Information gap decision theory with risk aversion strategy for robust planning of hybrid photovoltaic/wind/battery storage system in distribution networks considering uncertainty

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  • Boroumandfar, Gholamreza
  • Khajehzadeh, Alimorad
  • Eslami, Mahdiyeh
  • Syah, Rahmad B.Y.

Abstract

In this paper, robust planning of hybrid photovoltaic/wind/battery storage (PV/WT/Battery) system is performed in the distribution network to minimize power losses cost, purchasing power cost from the hybrid system as well as purchasing power cost from the upstream network considering uncertainties of network demand and renewable generation using information gap decision theory (IGDT) based on risk aversion strategy (RAS). The decision variables are considered as the optimal installation location and capacity of the hybrid system components in the network as well as the uncertainty radius of the uncertain parameters, which is determined using the flow direction algorithm (FDA), which is inspired by the movement of surface water flow to the pond outlet. The planning problem is implemented with deterministic and robust-based IGDT approaches. The proposed methodology is performed on the IEEE 33- and 69-bus distribution networks. The deterministic results indicate that the optimal hybrid PV/WT/Battery system planning in the networks significantly reduces system costs by injecting the optimal renewable power into the network and the battery power reserve management. The better capability of the FDA in deterministic planning is confirmed compared to the particle swarm optimization (PSO), crow search algorithm (CSA), and moth-flame optimizer (MFO) in achieving lower system cost. Moreover, in the IGDT robust approach, the maximum uncertainty radius of the renewable generation and network load is determined for the different uncertainties budget. The IGDT effectiveness is proved compared with deterministic and Monte Carlo simulation (MCS) methods. The total cost of the 33-bus network in deterministic, IGDT, and MCS is obtained at $ 17978.18, $ 20996.19, and $ 26178.02, respectively, and for the 69-bus network are achieved at $ 16846.26, $ 20215.39, and $ 20597.84, respectively. A comparison of the results showed that the MCS strongly depends on the defined scenarios, is very time-consuming, and is also not able to provide a level of cost assurance, the deterministic method is not able to determine the effect of uncertainties on system planning, but the IGDT can easily determine the cost of the system based on changes in uncertainty parameters. Also, planning based on the IGDT has resulted in the achievement of a robust hybrid system against forecasting errors caused by uncertainties.

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  • Boroumandfar, Gholamreza & Khajehzadeh, Alimorad & Eslami, Mahdiyeh & Syah, Rahmad B.Y., 2023. "Information gap decision theory with risk aversion strategy for robust planning of hybrid photovoltaic/wind/battery storage system in distribution networks considering uncertainty," Energy, Elsevier, vol. 278(PA).
    • Handle: RePEc:eee:energy:v:278:y:2023:i:pa:s0360544223011726
    DOI: 10.1016/j.energy.2023.127778
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    References listed on IDEAS

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    1. Sun, Qie & Fu, Yu & Lin, Haiyang & Wennersten, Ronald, 2022. "A novel integrated stochastic programming-information gap decision theory (IGDT) approach for optimization of integrated energy systems (IESs) with multiple uncertainties," Applied Energy, Elsevier, vol. 314(C).
    2. Younesi, Abdollah & Shayeghi, Hossein & Safari, Amin & Siano, Pierluigi, 2020. "Assessing the resilience of multi microgrid based widespread power systems against natural disasters using Monte Carlo Simulation," Energy, Elsevier, vol. 207(C).
    3. Zhang, Shenxi & Cheng, Haozhong & Li, Ke & Tai, Nengling & Wang, Dan & Li, Furong, 2018. "Multi-objective distributed generation planning in distribution network considering correlations among uncertainties," Applied Energy, Elsevier, vol. 226(C), pages 743-755.
    4. Jam, Alireza & Ardehali, Morteza Mohammadi & Hosseinian, Seyed Hossein, 2017. "A comprehensive approach for wind turbine generation allocation with accurate analysis of load curtailment using nested programming," Energy, Elsevier, vol. 133(C), pages 1063-1078.
    5. Majidi, M. & Mohammadi-Ivatloo, B. & Soroudi, A., 2019. "Application of information gap decision theory in practical energy problems: A comprehensive review," Applied Energy, Elsevier, vol. 249(C), pages 157-165.
    6. Ali, E.S. & Abd Elazim, S.M. & Abdelaziz, A.Y., 2017. "Ant Lion Optimization Algorithm for optimal location and sizing of renewable distributed generations," Renewable Energy, Elsevier, vol. 101(C), pages 1311-1324.
    7. Esmaeili, Mobin & Sedighizadeh, Mostafa & Esmaili, Masoud, 2016. "Multi-objective optimal reconfiguration and DG (Distributed Generation) power allocation in distribution networks using Big Bang-Big Crunch algorithm considering load uncertainty," Energy, Elsevier, vol. 103(C), pages 86-99.
    8. Li, Yang & Feng, Bo & Li, Guoqing & Qi, Junjian & Zhao, Dongbo & Mu, Yunfei, 2018. "Optimal distributed generation planning in active distribution networks considering integration of energy storage," Applied Energy, Elsevier, vol. 210(C), pages 1073-1081.
    9. Mohamad, Farihan & Teh, Jiashen & Lai, Ching-Ming, 2021. "Optimum allocation of battery energy storage systems for power grid enhanced with solar energy," Energy, Elsevier, vol. 223(C).
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    1. Ahmed A. Alguhi & Majed A. Alotaibi & Essam A. Al-Ammar, 2023. "Probabilistic Planning for an Energy Storage System Considering the Uncertainties in Smart Distribution Networks," Sustainability, MDPI, vol. 16(1), pages 1-23, December.

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