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Sustainable advanced distribution management system design considering differential pricing schemes and carbon emissions

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  • Tsao, Yu-Chung
  • Thanh, Vo-Van
  • Lu, Jye-Chyi

Abstract

Advancedd distribution management systems (ADMSs) integrate distributed generation (DG) units and battery energy storage systems (BESSs), and they are a promising solution to reduce the greenhouse gas emissions in the energy sector. This study addresses the sustainable ADMS (SADMS) design problem by determining the power flow optimal economic dispatches from traditional power plants, DG units, and BESSs to residential areas while maximizing the total profit. The designed SADMS provides a demand response programs with various energy pricing schemes that correspond to different customers and energy consumption loads. Three carbon emission policies (carbon tax, carbon cap, and carbon trade) are considered in the model. An advancedd two-phase (ATP) approach is proposed to solve the described problem. In the first phase, an artificial neuro-fuzzy system (ANFS) is developed based on self-learning and self-adjusting processes to determine the customer demand response loads and DG unit output energies in uncertain environments. A combined ANFS and optimization solver is proposed in the second phase to determine the optimal SADMS economic dispatch. The application of the proposed approach is examined using an empirical case study in Taiwan. The results demonstrate that the proposed ATP approach can determine the optimal economic dispatch with an extremely small deviation in demand response load of 0.92%. In addition, our approach increases the total profit (improving total profit by approximately 1.1%, 0.8%, and 1.9% for the three different carbon emission policy objective functions) and reduces the computational time (by 3.0–6.0 times) compared to those of the genetic algorithm. Finally, the proposed model illustrates that carbon trade is the best policy for improving the total SADMS profit while satisfying the given environmental constraints.

Suggested Citation

  • Tsao, Yu-Chung & Thanh, Vo-Van & Lu, Jye-Chyi, 2021. "Sustainable advanced distribution management system design considering differential pricing schemes and carbon emissions," Energy, Elsevier, vol. 219(C).
    • Handle: RePEc:eee:energy:v:219:y:2021:i:c:s0360544220327031
    DOI: 10.1016/j.energy.2020.119596
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    1. Sayyaadi, Hoseyn & Baghsheikhi, Mostafa, 2018. "Developing a novel methodology based on the adaptive neuro-fuzzy interference system for the exergoeconomic optimization of energy systems," Energy, Elsevier, vol. 164(C), pages 218-235.
    2. Duong Phan & Alireza Bab-Hadiashar & Reza Hoseinnezhad & Reza N. Jazar & Abhijit Date & Ali Jamali & Dinh Ba Pham & Hamid Khayyam, 2020. "Neuro-Fuzzy System for Energy Management of Conventional Autonomous Vehicles," Energies, MDPI, vol. 13(7), pages 1-16, April.
    3. Zakariazadeh, Alireza & Homaee, Omid & Jadid, Shahram & Siano, Pierluigi, 2014. "A new approach for real time voltage control using demand response in an automated distribution system," Applied Energy, Elsevier, vol. 117(C), pages 157-166.
    4. Li, Xue & Zhang, Rufeng & Bai, Linquan & Li, Guoqing & Jiang, Tao & Chen, Houhe, 2018. "Stochastic low-carbon scheduling with carbon capture power plants and coupon-based demand response," Applied Energy, Elsevier, vol. 210(C), pages 1219-1228.
    5. Pandey, Shashi Kant & Mohanty, Soumya R. & Kishor, Nand, 2013. "A literature survey on load–frequency control for conventional and distribution generation power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 318-334.
    6. Zhou, Y. & Li, Y.P. & Huang, G.H., 2015. "Planning sustainable electric-power system with carbon emission abatement through CDM under uncertainty," Applied Energy, Elsevier, vol. 140(C), pages 350-364.
    7. Phan, Duong & Bab-Hadiashar, Alireza & Lai, Chow Yin & Crawford, Bryn & Hoseinnezhad, Reza & Jazar, Reza N. & Khayyam, Hamid, 2020. "Intelligent energy management system for conventional autonomous vehicles," Energy, Elsevier, vol. 191(C).
    8. Hu, Kejia & Chen, Yuche, 2019. "Equilibrium fuel supply and carbon credit pricing under market competition and environmental regulations: A California case study," Applied Energy, Elsevier, vol. 236(C), pages 815-824.
    9. Jallal, Mohammed Ali & González-Vidal, Aurora & Skarmeta, Antonio F. & Chabaa, Samira & Zeroual, Abdelouhab, 2020. "A hybrid neuro-fuzzy inference system-based algorithm for time series forecasting applied to energy consumption prediction," Applied Energy, Elsevier, vol. 268(C).
    10. Tang, Rui & Wang, Shengwei & Li, Hangxin, 2019. "Game theory based interactive demand side management responding to dynamic pricing in price-based demand response of smart grids," Applied Energy, Elsevier, vol. 250(C), pages 118-130.
    11. Zuoyu Liu & Weimin Zheng & Feng Qi & Lei Wang & Bo Zou & Fushuan Wen & You Xue, 2018. "Optimal Dispatch of a Virtual Power Plant Considering Demand Response and Carbon Trading," Energies, MDPI, vol. 11(6), pages 1-19, June.
    12. Madia Safdar & Ghulam Amjad Hussain & Matti Lehtonen, 2019. "Costs of Demand Response from Residential Customers’ Perspective," Energies, MDPI, vol. 12(9), pages 1-16, April.
    13. Helbert Eduardo Espitia & Iván Machón-González & Hilario López-García & Guzmán Díaz, 2019. "Proposal of an Adaptive Neurofuzzy System to Control Flow Power in Distributed Generation Systems," Complexity, Hindawi, vol. 2019, pages 1-16, March.
    14. Yin, Xiuxing & Jiang, Zhansi & Pan, Li, 2020. "Recurrent neural network based adaptive integral sliding mode power maximization control for wind power systems," Renewable Energy, Elsevier, vol. 145(C), pages 1149-1157.
    15. Lu, Renzhi & Hong, Seung Ho, 2019. "Incentive-based demand response for smart grid with reinforcement learning and deep neural network," Applied Energy, Elsevier, vol. 236(C), pages 937-949.
    16. Davatgaran, Vahid & Saniei, Mohsen & Mortazavi, Seyed Saeidollah, 2019. "Smart distribution system management considering electrical and thermal demand response of energy hubs," Energy, Elsevier, vol. 169(C), pages 38-49.
    17. Tsao, Yu-Chung & Thanh, Vo-Van & Lu, Jye-Chyi, 2019. "Multiobjective robust fuzzy stochastic approach for sustainable smart grid design," Energy, Elsevier, vol. 176(C), pages 929-939.
    18. Zhitao Xu & Adel Elomri & Shaligram Pokharel & Fatih Mutlu, 2019. "The Design of Green Supply Chains under Carbon Policies: A Literature Review of Quantitative Models," Sustainability, MDPI, vol. 11(11), pages 1-20, May.
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