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A practical algorithm for distribution state estimation including renewable energy sources

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  • Niknam, Taher
  • Firouzi, Bahman Bahmani

Abstract

Renewable energy is energy that is in continuous supply over time. These kinds of energy sources are divided into five principal renewable sources of energy: the sun, the wind, flowing water, biomass and heat from within the earth. According to some studies carried out by the research institutes, about 25% of the new generation will be generated by Renewable Energy Sources (RESs) in the near future. Therefore, it is necessary to study the impact of RESs on the power systems, especially on the distribution networks. This paper presents a practical Distribution State Estimation (DSE) including RESs and some practical consideration. The proposed algorithm is based on the combination of Nelder–Mead simplex search and Particle Swarm Optimization (PSO) algorithms, called PSO-NM. The proposed algorithm can estimate load and RES output values by Weighted Least-Square (WLS) approach. Some practical considerations are var compensators, Voltage Regulators (VRs), Under Load Tap Changer (ULTC) transformer modeling, which usually have nonlinear and discrete characteristics, and unbalanced three-phase power flow equations. The comparison results with other evolutionary optimization algorithms such as original PSO, Honey Bee Mating Optimization (HBMO), Neural Networks (NNs), Ant Colony Optimization (ACO), and Genetic Algorithm (GA) for a test system demonstrate that PSO-NM is extremely effective and efficient for the DSE problems.

Suggested Citation

  • Niknam, Taher & Firouzi, Bahman Bahmani, 2009. "A practical algorithm for distribution state estimation including renewable energy sources," Renewable Energy, Elsevier, vol. 34(11), pages 2309-2316.
    • Handle: RePEc:eee:renene:v:34:y:2009:i:11:p:2309-2316
    DOI: 10.1016/j.renene.2009.03.005
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    References listed on IDEAS

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    1. Baños, R. & Manzano-Agugliaro, F. & Montoya, F.G. & Gil, C. & Alcayde, A. & Gómez, J., 2011. "Optimization methods applied to renewable and sustainable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 1753-1766, May.
    2. Khorshidi, Reza & Shabaninia, Faridon & Niknam, Taher, 2016. "A new smart approach for state estimation of distribution grids considering renewable energy sources," Energy, Elsevier, vol. 94(C), pages 29-37.
    3. Siddhartha, & Sharma, Naveen & Varun,, 2012. "A particle swarm optimization algorithm for optimization of thermal performance of a smooth flat plate solar air heater," Energy, Elsevier, vol. 38(1), pages 406-413.
    4. Honglu Zhu & Tingting Jiang & Yahui Sun & Shuang Sun, 2022. "A New Regional Distributed Photovoltaic Power Calculation Method Based on FCM-mRMR and nELM Model," Sustainability, MDPI, vol. 14(21), pages 1-20, October.
    5. Sepideh Radhoush & Maryam Bahramipanah & Hashem Nehrir & Zagros Shahooei, 2022. "A Review on State Estimation Techniques in Active Distribution Networks: Existing Practices and Their Challenges," Sustainability, MDPI, vol. 14(5), pages 1-16, February.
    6. Niknam, Taher, 2011. "A new HBMO algorithm for multiobjective daily Volt/Var control in distribution systems considering Distributed Generators," Applied Energy, Elsevier, vol. 88(3), pages 778-788, March.
    7. Miranda-Blanco, Blanca Nieves & Díaz-Dorado, Eloy & Carrillo, Camilo & Cidrás, J., 2014. "State estimation for wind farms including the wind turbine generator models," Renewable Energy, Elsevier, vol. 71(C), pages 453-465.
    8. Krystel K. Castillo-Villar, 2014. "Metaheuristic Algorithms Applied to Bioenergy Supply Chain Problems: Theory, Review, Challenges, and Future," Energies, MDPI, vol. 7(11), pages 1-33, November.
    9. Sharma, Naveen & Varun, & Siddhartha,, 2012. "Stochastic techniques used for optimization in solar systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(3), pages 1399-1411.
    10. Yong Zeng & Yanpeng Cai & Guohe Huang & Jing Dai, 2011. "A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty," Energies, MDPI, vol. 4(10), pages 1-33, October.
    11. Xydis, G., 2012. "Development of an integrated methodology for the energy needs of a major urban city: The case study of Athens, Greece," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(9), pages 6705-6716.
    12. Niknam, Taher & Meymand, Hamed Zeinoddini & Nayeripour, Majid, 2010. "A practical algorithm for optimal operation management of distribution network including fuel cell power plants," Renewable Energy, Elsevier, vol. 35(8), pages 1696-1714.
    13. Mortazi, Mohammad & Moradi, Ahmad & Khosravi, Mohsen, 2020. "Simultaneous optimization of transformer tap changer and network capacitors to improve the distribution system’s static security considering distributed generation sources," MPRA Paper 109052, University Library of Munich, Germany, revised 01 Jul 2020.
    14. Sepideh Radhoush & Bradley M. Whitaker & Hashem Nehrir, 2023. "An Overview of Supervised Machine Learning Approaches for Applications in Active Distribution Networks," Energies, MDPI, vol. 16(16), pages 1-29, August.

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