IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v195y2020ics0360544220301213.html
   My bibliography  Save this article

Economic-environmental dispatch of microgrid based on improved quantum particle swarm optimization

Author

Listed:
  • Xin-gang, Zhao
  • Ze-qi, Zhang
  • Yi-min, Xie
  • Jin, Meng

Abstract

As a practical choice to deal with energy security and low-carbon development, the microgrid can effectively promote the consumption of renewable energy. However, the volatility of renewable energy output affects the stable operation of microgrid. In order to ensure the reliability of power supply and improve the economy and environmental protection, this paper studied the short-term economic-environmental (EED) problem of the microgrid. Based on the dispatching model, this paper introduced the differential evolution (DE) into quantum particle swarm optimization (QPSO) and used the improved QPSO to solve problem. Through Friedman test, the performance is compared. The results show that: First, renewable energy should be preferred for generation, micro gas turbine take priority to meet thermal load, fuel cell, and storage battery are used for power supplement and power transaction. This plan can optimize the units output and reduce the emission of pollution gas on the premise of satisfying the regular operation of the microgrid. Second, in the later stage of improved QPSO, the ability to jump out of local optimal solution is stronger. Then, the improved QPSO can obtain better results. Therefore, the improved QPSO has better performance, and it is more suitable for solving microgrid EED problems than QPSO and CLQPSO.

Suggested Citation

  • Xin-gang, Zhao & Ze-qi, Zhang & Yi-min, Xie & Jin, Meng, 2020. "Economic-environmental dispatch of microgrid based on improved quantum particle swarm optimization," Energy, Elsevier, vol. 195(C).
    • Handle: RePEc:eee:energy:v:195:y:2020:i:c:s0360544220301213
    DOI: 10.1016/j.energy.2020.117014
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220301213
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.117014?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fan, Guo-Feng & Peng, Li-Ling & Hong, Wei-Chiang, 2018. "Short term load forecasting based on phase space reconstruction algorithm and bi-square kernel regression model," Applied Energy, Elsevier, vol. 224(C), pages 13-33.
    2. Nunes, H.G.G. & Pombo, J.A.N. & Mariano, S.J.P.S. & Calado, M.R.A. & Felippe de Souza, J.A.M., 2018. "A new high performance method for determining the parameters of PV cells and modules based on guaranteed convergence particle swarm optimization," Applied Energy, Elsevier, vol. 211(C), pages 774-791.
    3. Feng, Zhong-kai & Niu, Wen-jing & Cheng, Chun-tian, 2017. "Multi-objective quantum-behaved particle swarm optimization for economic environmental hydrothermal energy system scheduling," Energy, Elsevier, vol. 131(C), pages 165-178.
    4. Yongquan Dong & Zichen Zhang & Wei-Chiang Hong, 2018. "A Hybrid Seasonal Mechanism with a Chaotic Cuckoo Search Algorithm with a Support Vector Regression Model for Electric Load Forecasting," Energies, MDPI, vol. 11(4), pages 1-21, April.
    5. Chakraborty, Uday K. & Abbott, Travis E. & Das, Sajal K., 2012. "PEM fuel cell modeling using differential evolution," Energy, Elsevier, vol. 40(1), pages 387-399.
    6. Hui Wang & Tengxin Wang & Xiaohan Xie & Zhixiang Ling & Guoliang Gao & Xu Dong, 2018. "Optimal Capacity Configuration of a Hybrid Energy Storage System for an Isolated Microgrid Using Quantum-Behaved Particle Swarm Optimization," Energies, MDPI, vol. 11(2), pages 1-14, February.
    7. Glotić, Arnel & Zamuda, Aleš, 2015. "Short-term combined economic and emission hydrothermal optimization by surrogate differential evolution," Applied Energy, Elsevier, vol. 141(C), pages 42-56.
    8. Biswas, Partha P. & Suganthan, P.N. & Qu, B.Y. & Amaratunga, Gehan A.J., 2018. "Multiobjective economic-environmental power dispatch with stochastic wind-solar-small hydro power," Energy, Elsevier, vol. 150(C), pages 1039-1057.
    9. Xin-gang, Zhao & Zhen, Wang, 2019. "Technology, cost, economic performance of distributed photovoltaic industry in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 53-64.
    10. Younes, Mimoun & Khodja, Fouad & Kherfane, Riad Lakhdar, 2014. "Multi-objective economic emission dispatch solution using hybrid FFA (firefly algorithm) and considering wind power penetration," Energy, Elsevier, vol. 67(C), pages 595-606.
    11. Gherbi, Yamina Ahlem & Bouzeboudja, Hamid & Gherbi, Fatima Zohra, 2016. "The combined economic environmental dispatch using new hybrid metaheuristic," Energy, Elsevier, vol. 115(P1), pages 468-477.
    12. Novoa, Laura & Flores, Robert & Brouwer, Jack, 2019. "Optimal renewable generation and battery storage sizing and siting considering local transformer limits," Applied Energy, Elsevier, vol. 256(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Qian, Tong & Tang, Wenhu & Wu, Qinghua, 2020. "A fully decentralized dual consensus method for carbon trading power dispatch with wind power," Energy, Elsevier, vol. 203(C).
    2. Hwang Goh, Hui & Shi, Shuaiwei & Liang, Xue & Zhang, Dongdong & Dai, Wei & Liu, Hui & Yuong Wong, Shen & Agustiono Kurniawan, Tonni & Chen Goh, Kai & Leei Cham, Chin, 2022. "Optimal energy scheduling of grid-connected microgrids with demand side response considering uncertainty," Applied Energy, Elsevier, vol. 327(C).
    3. Hani Muhsen & Asma Alkhraibat & Ala’aldeen Al-Halhouli, 2023. "Real-Time Simulation and Energy Management Attainment of Microgrids," Sustainability, MDPI, vol. 15(3), pages 1-16, February.
    4. Marcelino, C.G. & Leite, G.M.C. & Wanner, E.F. & Jiménez-Fernández, S. & Salcedo-Sanz, S., 2023. "Evaluating the use of a Net-Metering mechanism in microgrids to reduce power generation costs with a swarm-intelligent algorithm," Energy, Elsevier, vol. 266(C).
    5. Sandelic, Monika & Peyghami, Saeed & Sangwongwanich, Ariya & Blaabjerg, Frede, 2022. "Reliability aspects in microgrid design and planning: Status and power electronics-induced challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    6. Abdelmalek, Samir & Dali, Ali & Bakdi, Azzeddine & Bettayeb, Maamar, 2020. "Design and experimental implementation of a new robust observer-based nonlinear controller for DC-DC buck converters," Energy, Elsevier, vol. 213(C).
    7. El-Sayed, Wael T. & El-Saadany, Ehab F. & Zeineldin, Hatem H. & Al-Sumaiti, Ameena S., 2020. "Fast initialization methods for the nonconvex economic dispatch problem," Energy, Elsevier, vol. 201(C).
    8. Peng, Simin & Sun, Yunxiang & Liu, Dandan & Yu, Quanqing & Kan, Jiarong & Pecht, Michael, 2023. "State of health estimation of lithium-ion batteries based on multi-health features extraction and improved long short-term memory neural network," Energy, Elsevier, vol. 282(C).
    9. Zhang, M.Y. & Chen, J.J. & Yang, Z.J. & Peng, K. & Zhao, Y.L. & Zhang, X.H., 2021. "Stochastic day-ahead scheduling of irrigation system integrated agricultural microgrid with pumped storage and uncertain wind power," Energy, Elsevier, vol. 237(C).
    10. Ahmed, Ijaz & Rehan, Muhammad & Basit, Abdul & Malik, Saddam Hussain & Alvi, Um-E-Habiba & Hong, Keum-Shik, 2022. "Multi-area economic emission dispatch for large-scale multi-fueled power plants contemplating inter-connected grid tie-lines power flow limitations," Energy, Elsevier, vol. 261(PB).
    11. Xu, Shengping & Xiong, Guojiang & Mohamed, Ali Wagdy & Bouchekara, Houssem R.E.H., 2022. "Forgetting velocity based improved comprehensive learning particle swarm optimization for non-convex economic dispatch problems with valve-point effects and multi-fuel options," Energy, Elsevier, vol. 256(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kailai Ni & Jianzhou Wang & Guangyu Tang & Danxiang Wei, 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia," Energies, MDPI, vol. 12(13), pages 1-30, June.
    2. Mei-Li Shen & Cheng-Feng Lee & Hsiou-Hsiang Liu & Po-Yin Chang & Cheng-Hong Yang, 2021. "An Effective Hybrid Approach for Forecasting Currency Exchange Rates," Sustainability, MDPI, vol. 13(5), pages 1-29, March.
    3. Ziad M. Ali & Shady H. E. Abdel Aleem & Ahmed I. Omar & Bahaa Saad Mahmoud, 2022. "Economical-Environmental-Technical Operation of Power Networks with High Penetration of Renewable Energy Systems Using Multi-Objective Coronavirus Herd Immunity Algorithm," Mathematics, MDPI, vol. 10(7), pages 1-43, April.
    4. Wei-Chiang Hong & Guo-Feng Fan, 2019. "Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting," Energies, MDPI, vol. 12(6), pages 1-16, March.
    5. Wang, Guibin & Zha, Yongxing & Wu, Ting & Qiu, Jing & Peng, Jian-chun & Xu, Gang, 2020. "Cross entropy optimization based on decomposition for multi-objective economic emission dispatch considering renewable energy generation uncertainties," Energy, Elsevier, vol. 193(C).
    6. Guo-Feng Fan & Yan-Hui Guo & Jia-Mei Zheng & Wei-Chiang Hong, 2019. "Application of the Weighted K-Nearest Neighbor Algorithm for Short-Term Load Forecasting," Energies, MDPI, vol. 12(5), pages 1-19, March.
    7. Sungwoo Park & Jihoon Moon & Seungwon Jung & Seungmin Rho & Sung Wook Baik & Eenjun Hwang, 2020. "A Two-Stage Industrial Load Forecasting Scheme for Day-Ahead Combined Cooling, Heating and Power Scheduling," Energies, MDPI, vol. 13(2), pages 1-23, January.
    8. Tongxiang Liu & Yu Jin & Yuyang Gao, 2019. "A New Hybrid Approach for Short-Term Electric Load Forecasting Applying Support Vector Machine with Ensemble Empirical Mode Decomposition and Whale Optimization," Energies, MDPI, vol. 12(8), pages 1-20, April.
    9. Zhu, Xiaoyue & Dang, Yaoguo & Ding, Song, 2020. "Using a self-adaptive grey fractional weighted model to forecast Jiangsu’s electricity consumption in China," Energy, Elsevier, vol. 190(C).
    10. Shahzad Aslam & Nasir Ayub & Umer Farooq & Muhammad Junaid Alvi & Fahad R. Albogamy & Gul Rukh & Syed Irtaza Haider & Ahmad Taher Azar & Rasool Bukhsh, 2021. "Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid," Sustainability, MDPI, vol. 13(22), pages 1-28, November.
    11. Singh, Priyanka & Dwivedi, Pragya & Kant, Vibhor, 2019. "A hybrid method based on neural network and improved environmental adaptation method using Controlled Gaussian Mutation with real parameter for short-term load forecasting," Energy, Elsevier, vol. 174(C), pages 460-477.
    12. Shin, Hansol & Kim, Tae Hyun & Kim, Hyoungtae & Lee, Sungwoo & Kim, Wook, 2019. "Environmental shutdown of coal-fired generators for greenhouse gas reduction: A case study of South Korea," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    13. Yuval Beck & Ram Machlev, 2019. "Harmonic Loads Classification by Means of Currents’ Physical Components," Energies, MDPI, vol. 12(21), pages 1-18, October.
    14. Zhang, Xian & Wang, Huaizhi & Peng, Jian-chun & Liu, Yitao & Wang, Guibin & Jiang, Hui, 2018. "GPNBI inspired MOSDE for electric power dispatch considering wind energy penetration," Energy, Elsevier, vol. 144(C), pages 404-419.
    15. Cheng-Hong Yang & Po-Yin Chang, 2020. "Forecasting the Demand for Container Throughput Using a Mixed-Precision Neural Architecture Based on CNN–LSTM," Mathematics, MDPI, vol. 8(10), pages 1-17, October.
    16. Libo Zhang & Qian Du & Dequn Zhou, 2021. "Grid Parity Analysis of China’s Centralized Photovoltaic Generation under Multiple Uncertainties," Energies, MDPI, vol. 14(7), pages 1-19, March.
    17. Feng, Zhong-kai & Niu, Wen-jing & Wang, Wen-chuan & Zhou, Jian-zhong & Cheng, Chun-tian, 2019. "A mixed integer linear programming model for unit commitment of thermal plants with peak shaving operation aspect in regional power grid lack of flexible hydropower energy," Energy, Elsevier, vol. 175(C), pages 618-629.
    18. Nwulu, Nnamdi I. & Xia, Xiaohua, 2015. "Implementing a model predictive control strategy on the dynamic economic emission dispatch problem with game theory based demand response programs," Energy, Elsevier, vol. 91(C), pages 404-419.
    19. Vrionis, Constantinos & Tsalavoutis, Vasilios & Tolis, Athanasios, 2020. "A Generation Expansion Planning model for integrating high shares of renewable energy: A Meta-Model Assisted Evolutionary Algorithm approach," Applied Energy, Elsevier, vol. 259(C).
    20. Jihoon Moon & Junhong Kim & Pilsung Kang & Eenjun Hwang, 2020. "Solving the Cold-Start Problem in Short-Term Load Forecasting Using Tree-Based Methods," Energies, MDPI, vol. 13(4), pages 1-37, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:195:y:2020:i:c:s0360544220301213. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.