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Least cost generation expansion planning with solar power plant using Differential Evolution algorithm

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  • Rajesh, K.
  • Bhuvanesh, A.
  • Kannan, S.
  • Thangaraj, C.

Abstract

The issue of balance between the benefits of greater renewable penetration with the cost of adapting conventional base load systems is drawing the attention of power system planners across the globe. The study region, State of Tamilnadu, India, faces acute power shortages and frequent power cuts, though the installed capacity of the state is higher than the peak demand and is planning more solar additions. The impact of the inclusion of solar power plants is analyzed, for 6-year and 14-year planning horizons, using the model formulated, integrating all critical elements of the system, employing Differential Evolution (DE) algorithm. A balanced approach is adopted to understand the long term impact of solar additions by realistically imposing Total Emission Reductions Constraints (TERC), and Emission Treatment Penalty Costs (ETPC) on the remaining portion of pollution. The sensitivity of the system generation mix and the system reliability, to different solar power development and emissions reduction scenarios is also studied. The resulting variations in different cost components are also reported. The study will have greater utility for the planners who are currently involved in the long term planning of systems expected to have increasing proportion of RET plants.

Suggested Citation

  • Rajesh, K. & Bhuvanesh, A. & Kannan, S. & Thangaraj, C., 2016. "Least cost generation expansion planning with solar power plant using Differential Evolution algorithm," Renewable Energy, Elsevier, vol. 85(C), pages 677-686.
    • Handle: RePEc:eee:renene:v:85:y:2016:i:c:p:677-686
    DOI: 10.1016/j.renene.2015.07.026
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    References listed on IDEAS

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    1. Berna Tektaş & Hasan Hüseyin Turan & Nihat Kasap & Ferhan Çebi & Dursun Delen, 2022. "A Fuzzy Prescriptive Analytics Approach to Power Generation Capacity Planning," Energies, MDPI, vol. 15(9), pages 1-26, April.
    2. Sadeghi, Hadi & Rashidinejad, Masoud & Abdollahi, Amir, 2017. "A comprehensive sequential review study through the generation expansion planning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 1369-1394.
    3. Biswas (Raha), Syamasree & Mandal, Kamal Krishna & Chakraborty, Niladri, 2016. "Pareto-efficient double auction power transactions for economic reactive power dispatch," Applied Energy, Elsevier, vol. 168(C), pages 610-627.
    4. Hemmati, Reza & Saboori, Hedayat & Jirdehi, Mehdi Ahmadi, 2016. "Multistage generation expansion planning incorporating large scale energy storage systems and environmental pollution," Renewable Energy, Elsevier, vol. 97(C), pages 636-645.
    5. Javadi, Mohammad Sadegh & Razavi, Seyed-Ehsan & Ahmadi, Abdollah & Siano, Pierluigi, 2019. "A novel approach for distant wind farm interconnection: Iran South-West wind farms integration," Renewable Energy, Elsevier, vol. 140(C), pages 737-750.
    6. Zhang, Jingrui & Tang, Qinghui & Chen, Yalin & Lin, Shuang, 2016. "A hybrid particle swarm optimization with small population size to solve the optimal short-term hydro-thermal unit commitment problem," Energy, Elsevier, vol. 109(C), pages 765-780.
    7. Carlos Roberto de Sousa Costa & Paula Ferreira, 2023. "A Review on the Internalization of Externalities in Electricity Generation Expansion Planning," Energies, MDPI, vol. 16(4), pages 1-19, February.
    8. Fitiwi, Desta Z. & Lynch, Muireann & Bertsch, Valentin, 2020. "Enhanced network effects and stochastic modelling in generation expansion planning: Insights from an insular power system," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    9. Zhang, Jingrui & Lin, Shuang & Liu, Houde & Chen, Yalin & Zhu, Mingcheng & Xu, Yinliang, 2017. "A small-population based parallel differential evolution algorithm for short-term hydrothermal scheduling problem considering power flow constraints," Energy, Elsevier, vol. 123(C), pages 538-554.
    10. Shilaja, C. & Ravi, K., 2017. "Optimization of emission/economic dispatch using euclidean affine flower pollination algorithm (eFPA) and binary FPA (BFPA) in solar photo voltaic generation," Renewable Energy, Elsevier, vol. 107(C), pages 550-566.
    11. Majid Dehghani & Mohammad Taghipour & Saleh Sadeghi Gougheri & Amirhossein Nikoofard & Gevork B. Gharehpetian & Mahdi Khosravy, 2021. "A Deep Learning-Based Approach for Generation Expansion Planning Considering Power Plants Lifetime," Energies, MDPI, vol. 14(23), pages 1-21, December.
    12. Rajesh, K. & Karthikeyan, K. & Kannan, S. & Thangaraj, C., 2016. "Generation expansion planning based on solar plants with storage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 953-964.

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