IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v220y2024ics0960148123015367.html
   My bibliography  Save this article

A new optimization approach considering demand response management and multistage energy storage: A novel perspective for Fujian Province

Author

Listed:
  • Zheng, Xidong
  • Chen, Huangbin
  • Jin, Tao

Abstract

Due to the inherent uncertainty and the mismatch between renewable energy output and load demand, renewable energy-based system optimization is increasingly important in China. Therefore, electricity demand response (EDR) is critical to the stability and efficiency of an integrated renewable energy system (IRES). The customers' demand response under the time-of-use (TOU) mechanism is the key to the entire process. However, how to optimize integrated renewable energy system based on accurately identifying the electricity demand response participation of customers is one of the main issues at this stage. To solve the problem, this paper presents a novel approach for integrated renewable energy system optimization considering electricity demand response management and multistage energy storage systems from the perspective of Fujian Province, China. To enable large-scale renewable energy integration, the original output is processed using the Hampel identifier (HI)-Savitzky-Golay filter (SGF) technology. A machine learning (ML)-based identification model is developed for EDR to determine whether the customers are participating or not. Driven by the time-of-use mechanism, customers can make dynamic adjustments based on the Logistics function. After that, an integrated renewable energy system model optimization strategy is proposed based on satisfying customers’ electricity demand response. Throughout the optimization process, the multistage energy storage system plays a vital role in the residual fluctuation absorption for renewable energy filtering, the dynamic adjustment process of the demand response, and the integrated renewable energy system optimization. Through extensive case studies and discussions, it is demonstrated that random forest (RF) and support vector machine (SVM) reach 80 % identification accuracy with a 20 % testing ratio. According to the optimization approach proposed in this paper, the minimum operation cost can be calculated to be 9.336*106 CNY. The methodology presented in this paper establishes a special relationship between electricity demand response identification and dynamic adjustments, and provides an optimization model for Fujian Province, China, which is a reliable reference for future work.

Suggested Citation

  • Zheng, Xidong & Chen, Huangbin & Jin, Tao, 2024. "A new optimization approach considering demand response management and multistage energy storage: A novel perspective for Fujian Province," Renewable Energy, Elsevier, vol. 220(C).
    • Handle: RePEc:eee:renene:v:220:y:2024:i:c:s0960148123015367
    DOI: 10.1016/j.renene.2023.119621
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148123015367
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2023.119621?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. Wang, Yuqing & Wehrle, Lukas & Banerjee, Aayan & Shi, Yixiang & Deutschmann, Olaf, 2021. "Analysis of a biogas-fed SOFC CHP system based on multi-scale hierarchical modeling," Renewable Energy, Elsevier, vol. 163(C), pages 78-87.
    2. Ahmad, Tanveer & Madonski, Rafal & Zhang, Dongdong & Huang, Chao & Mujeeb, Asad, 2022. "Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    3. Das, Barun K. & Hasan, Mahmudul & Das, Pronob, 2021. "Impact of storage technologies, temporal resolution, and PV tracking on stand-alone hybrid renewable energy for an Australian remote area application," Renewable Energy, Elsevier, vol. 173(C), pages 362-380.
    4. Zheng, Xidong & Bai, Feifei & Zhuang, Zhiyuan & Chen, Zixing & Jin, Tao, 2023. "A new demand response management strategy considering renewable energy prediction and filtering technology," Renewable Energy, Elsevier, vol. 211(C), pages 656-668.
    5. Karaca, Ali Erdogan & Dincer, Ibrahim & Nitefor, Michael, 2023. "A new renewable energy system integrated with compressed air energy storage and multistage desalination," Energy, Elsevier, vol. 268(C).
    6. Qu, Zongxi & Mao, Wenqian & Zhang, Kequan & Zhang, Wenyu & Li, Zhipeng, 2019. "Multi-step wind speed forecasting based on a hybrid decomposition technique and an improved back-propagation neural network," Renewable Energy, Elsevier, vol. 133(C), pages 919-929.
    7. Stevens, Kelly A. & Tang, Tian & Hittinger, Eric, 2023. "Innovation in complementary energy technologies from renewable energy policies," Renewable Energy, Elsevier, vol. 209(C), pages 431-441.
    8. Wen, Shuqing & Zhang, Weirong & Sun, Yifu & Li, Zhenxi & Huang, Boju & Bian, Shouguo & Zhao, Lin & Wang, Yan, 2023. "An enhanced principal component analysis method with Savitzky–Golay filter and clustering algorithm for sensor fault detection and diagnosis," Applied Energy, Elsevier, vol. 337(C).
    9. Guo, Fei & Wu, Xiongwei & Liu, Lili & Ye, Jilei & Wang, Tao & Fu, Lijun & Wu, Yuping, 2023. "Prediction of remaining useful life and state of health of lithium batteries based on time series feature and Savitzky-Golay filter combined with gated recurrent unit neural network," Energy, Elsevier, vol. 270(C).
    10. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
    11. Bu, Xiangya & Wu, Qiuwei & Zhou, Bin & Li, Canbing, 2023. "Hybrid short-term load forecasting using CGAN with CNN and semi-supervised regression," Applied Energy, Elsevier, vol. 338(C).
    12. Wang, Xuerui & Li, Xiangyu & Li, Shaoting, 2022. "Point and interval forecasting system for crude oil price based on complete ensemble extreme-point symmetric mode decomposition with adaptive noise and intelligent optimization algorithm," Applied Energy, Elsevier, vol. 328(C).
    13. Yang, Xiaohui & Wang, Xiaopeng & Leng, Zhengyang & Deng, Yeheng & Deng, Fuwei & Zhang, Zhonglian & Yang, Li & Liu, Xiaoping, 2023. "An optimized scheduling strategy combining robust optimization and rolling optimization to solve the uncertainty of RES-CCHP MG," Renewable Energy, Elsevier, vol. 211(C), pages 307-325.
    14. Agostini, Claudio A. & Armijo, Franco A. & Silva, Carlos & Nasirov, Shahriyar, 2021. "The role of frequency regulation remuneration schemes in an energy matrix with high penetration of renewable energy," Renewable Energy, Elsevier, vol. 171(C), pages 1097-1114.
    15. Izanloo, Milad & Aslani, Alireza & Zahedi, Rahim, 2022. "Development of a Machine learning assessment method for renewable energy investment decision making," Applied Energy, Elsevier, vol. 327(C).
    16. Meenal, R. & Selvakumar, A. Immanuel, 2018. "Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters," Renewable Energy, Elsevier, vol. 121(C), pages 324-343.
    17. Ngoc-Lan Huynh, Anh & Deo, Ravinesh C. & Ali, Mumtaz & Abdulla, Shahab & Raj, Nawin, 2021. "Novel short-term solar radiation hybrid model: Long short-term memory network integrated with robust local mean decomposition," Applied Energy, Elsevier, vol. 298(C).
    18. Ma, Zherui & Dong, Fuxiang & Wang, Jiangjiang & Zhou, Yuan & Feng, Yingsong, 2023. "Optimal design of a novel hybrid renewable energy CCHP system considering long and short-term benefits," Renewable Energy, Elsevier, vol. 206(C), pages 72-85.
    19. Wu, Huijuan & Meng, Keqilao & Fan, Daoerji & Zhang, Zhanqiang & Liu, Qing, 2022. "Multistep short-term wind speed forecasting using transformer," Energy, Elsevier, vol. 261(PA).
    20. Zhang, Yagang & Pan, Guifang & Chen, Bing & Han, Jingyi & Zhao, Yuan & Zhang, Chenhong, 2020. "Short-term wind speed prediction model based on GA-ANN improved by VMD," Renewable Energy, Elsevier, vol. 156(C), pages 1373-1388.
    21. Teng, Wei & Ding, Xian & Cheng, Hao & Han, Chen & Liu, Yibing & Mu, Haihua, 2019. "Compound faults diagnosis and analysis for a wind turbine gearbox via a novel vibration model and empirical wavelet transform," Renewable Energy, Elsevier, vol. 136(C), pages 393-402.
    22. Mellit, A. & Benghanem, M. & Kalogirou, S.A., 2007. "Modeling and simulation of a stand-alone photovoltaic system using an adaptive artificial neural network: Proposition for a new sizing procedure," Renewable Energy, Elsevier, vol. 32(2), pages 285-313.
    23. Yu, Xinran & Ergan, Semiha, 2022. "Estimating power demand shaving capacity of buildings on an urban scale using extracted demand response profiles through machine learning models," Applied Energy, Elsevier, vol. 310(C).
    24. Lu, Peng & Ye, Lin & Tang, Yong & Zhao, Yongning & Zhong, Wuzhi & Qu, Ying & Zhai, Bingxu, 2021. "Ultra-short-term combined prediction approach based on kernel function switch mechanism," Renewable Energy, Elsevier, vol. 164(C), pages 842-866.
    25. Banguero, Edison & Correcher, Antonio & Pérez-Navarro, Ángel & García, Emilio & Aristizabal, Andrés, 2020. "Diagnosis of a battery energy storage system based on principal component analysis," Renewable Energy, Elsevier, vol. 146(C), pages 2438-2449.
    26. Lu, Xinhui & Li, Haobin & Zhou, Kaile & Yang, Shanlin, 2023. "Optimal load dispatch of energy hub considering uncertainties of renewable energy and demand response," Energy, Elsevier, vol. 262(PB).
    27. Astriani, Yuli & Shafiullah, GM & Shahnia, Farhad, 2021. "Incentive determination of a demand response program for microgrids," Applied Energy, Elsevier, vol. 292(C).
    28. Ding, Yihong & Tan, Qinliang & Shan, Zijing & Han, Jian & Zhang, Yimei, 2023. "A two-stage dispatching optimization strategy for hybrid renewable energy system with low-carbon and sustainability in ancillary service market," Renewable Energy, Elsevier, vol. 207(C), pages 647-659.
    29. Yuan, Wenlin & Xin, Wenpeng & Su, Chengguo & Cheng, Chuntian & Yan, Denghua & Wu, Zening, 2022. "Cross-regional integrated transmission of wind power and pumped-storage hydropower considering the peak shaving demands of multiple power grids," Renewable Energy, Elsevier, vol. 190(C), pages 1112-1126.
    30. Carmichael, R. & Gross, R. & Hanna, R. & Rhodes, A. & Green, T., 2021. "The Demand Response Technology Cluster: Accelerating UK residential consumer engagement with time-of-use tariffs, electric vehicles and smart meters via digital comparison tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    31. He, Yi & Guo, Su & Zhou, Jianxu & Ye, Jilei & Huang, Jing & Zheng, Kun & Du, Xinru, 2022. "Multi-objective planning-operation co-optimization of renewable energy system with hybrid energy storages," Renewable Energy, Elsevier, vol. 184(C), pages 776-790.
    32. Shi, Renwei & Jiao, Zaibin, 2023. "Individual household demand response potential evaluation and identification based on machine learning algorithms," Energy, Elsevier, vol. 266(C).
    33. Ghasemi, Ahmad & Enayatzare, Mehdi, 2018. "Optimal energy management of a renewable-based isolated microgrid with pumped-storage unit and demand response," Renewable Energy, Elsevier, vol. 123(C), pages 460-474.
    34. Meng, Yan & Fan, Shuai & Shen, Yu & Xiao, Jucheng & He, Guangyu & Li, Zuyi, 2023. "Transmission and distribution network-constrained large-scale demand response based on locational customer directrix load for accommodating renewable energy," Applied Energy, Elsevier, vol. 350(C).
    35. Wang, Fei & Lu, Xiaoxing & Chang, Xiqiang & Cao, Xin & Yan, Siqing & Li, Kangping & Duić, Neven & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "Household profile identification for behavioral demand response: A semi-supervised learning approach using smart meter data," Energy, Elsevier, vol. 238(PB).
    36. Cambron, P. & Lepvrier, R. & Masson, C. & Tahan, A. & Pelletier, F., 2016. "Power curve monitoring using weighted moving average control charts," Renewable Energy, Elsevier, vol. 94(C), pages 126-135.
    37. Wang, Shuhang & Wang, Xu & Fu, Zhenghui & Liu, Feng & Xu, Ye & Li, Wei, 2022. "A novel energy-water nexus based CHP operation optimization model under water shortage," Energy, Elsevier, vol. 239(PA).
    38. Micheli, Leonardo & Theristis, Marios & Talavera, Diego L. & Nofuentes, Gustavo & Stein, Joshua S. & Almonacid, Florencia & Fernández, Eduardo F., 2022. "The economic value of photovoltaic performance loss mitigation in electricity spot markets," Renewable Energy, Elsevier, vol. 199(C), pages 486-497.
    Full references (including those not matched with items on IDEAS)

    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. Zheng, Xidong & Zhou, Sheng & Jin, Tao, 2023. "A new machine learning-based approach for cross-region coupled wind-storage integrated systems identification considering electricity demand response and data integration: A new provincial perspective," Energy, Elsevier, vol. 283(C).
    2. Mahfoud, Rabea Jamil & Alkayem, Nizar Faisal & Zhang, Yuquan & Zheng, Yuan & Sun, Yonghui & Alhelou, Hassan Haes, 2023. "Optimal operation of pumped hydro storage-based energy systems: A compendium of current challenges and future perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 178(C).
    3. Li, Yanhui & Sun, Kaixuan & Yao, Qi & Wang, Lin, 2024. "A dual-optimization wind speed forecasting model based on deep learning and improved dung beetle optimization algorithm," Energy, Elsevier, vol. 286(C).
    4. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    5. Zheng, Xidong & Bai, Feifei & Zeng, Ziyang & Jin, Tao, 2024. "A new methodology to improve wind power prediction accuracy considering power quality disturbance dimension reduction and elimination," Energy, Elsevier, vol. 287(C).
    6. Stefenon, Stefano Frizzo & Seman, Laio Oriel & Aquino, Luiza Scapinello & Coelho, Leandro dos Santos, 2023. "Wavelet-Seq2Seq-LSTM with attention for time series forecasting of level of dams in hydroelectric power plants," Energy, Elsevier, vol. 274(C).
    7. Seshu Kumar, R. & Phani Raghav, L. & Koteswara Raju, D. & Singh, Arvind R., 2021. "Impact of multiple demand side management programs on the optimal operation of grid-connected microgrids," Applied Energy, Elsevier, vol. 301(C).
    8. Phani Raghav, L. & Seshu Kumar, R. & Koteswara Raju, D. & Singh, Arvind R., 2022. "Analytic Hierarchy Process (AHP) – Swarm intelligence based flexible demand response management of grid-connected microgrid," Applied Energy, Elsevier, vol. 306(PB).
    9. Izabela Jonek-Kowalska & Sara Rupacz, 2023. "The Innovative Nature of Selected Polish Companies in the Energy Sector Compared to the Use of Renewable Energy Sources from a Financial and an Investor’s Perspective," Resources, MDPI, vol. 12(12), pages 1-19, December.
    10. Liu, Xingdou & Zhang, Li & Wang, Jiangong & Zhou, Yue & Gan, Wei, 2023. "A unified multi-step wind speed forecasting framework based on numerical weather prediction grids and wind farm monitoring data," Renewable Energy, Elsevier, vol. 211(C), pages 948-963.
    11. Md Masud Rana & Mohamed Atef & Md Rasel Sarkar & Moslem Uddin & GM Shafiullah, 2022. "A Review on Peak Load Shaving in Microgrid—Potential Benefits, Challenges, and Future Trend," Energies, MDPI, vol. 15(6), pages 1-17, March.
    12. Khan, Noman & Khan, Samee Ullah & Baik, Sung Wook, 2023. "Deep dive into hybrid networks: A comparative study and novel architecture for efficient power prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    13. Liu, Yixing & Liu, Bo & Guo, Xiaoyu & Xu, Yiqiao & Ding, Zhengtao, 2023. "Household profile identification for retailers based on personalized federated learning," Energy, Elsevier, vol. 275(C).
    14. Ma, Jinjin & Yang, Lin & Wang, Donghan & Li, Yiming & Xie, Zuomiao & Lv, Haodong & Woo, Donghyup, 2024. "Digitalization in response to carbon neutrality: Mechanisms, effects and prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    15. Zhang, Meng & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2022. "A novel flexible grey multivariable model and its application in forecasting energy consumption in China," Energy, Elsevier, vol. 239(PE).
    16. Sumit Saroha & Marta Zurek-Mortka & Jerzy Ryszard Szymanski & Vineet Shekher & Pardeep Singla, 2021. "Forecasting of Market Clearing Volume Using Wavelet Packet-Based Neural Networks with Tracking Signals," Energies, MDPI, vol. 14(19), pages 1-21, September.
    17. Zhu, Xiaoxun & Liu, Ruizhang & Chen, Yao & Gao, Xiaoxia & Wang, Yu & Xu, Zixu, 2021. "Wind speed behaviors feather analysis and its utilization on wind speed prediction using 3D-CNN," Energy, Elsevier, vol. 236(C).
    18. Lu, Yunbo & Wang, Lunche & Zhu, Canming & Zou, Ling & Zhang, Ming & Feng, Lan & Cao, Qian, 2023. "Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    19. Manish Kumar Singla & Jyoti Gupta & Mohammed H. Alsharif & Abu Jahid, 2023. "Optimizing Integration of Fuel Cell Technology in Renewable Energy-Based Microgrids for Sustainable and Cost-Effective Energy," Energies, MDPI, vol. 16(11), pages 1-18, June.
    20. Germán Arana-Landín & Naiara Uriarte-Gallastegi & Beñat Landeta-Manzano & Iker Laskurain-Iturbe, 2023. "The Contribution of Lean Management—Industry 4.0 Technologies to Improving Energy Efficiency," Energies, MDPI, vol. 16(5), pages 1-19, 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:renene:v:220:y:2024:i:c:s0960148123015367. 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/renewable-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.