IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v263y2023ipbs0360544222026706.html
   My bibliography  Save this article

ELCC-based capacity value estimation of combined wind - storage system using IPSO algorithm

Author

Listed:
  • Wen, Lei
  • Song, Qianqian

Abstract

Combined wind-storage systems (CWSSs) could significantly improve the reliability of power systems. In order to quantify the contribution of wind power and storage systems on adequacy of power systems, this paper proposes a novel algorithm based on equivalent load carrying capacity (ELCC), improved particle swarm optimization (IPSO). Simulation results are also provided. First, a novel algorithm IPSO for evaluating ELCC is presented. Second, the ELCC of wind power under different wind power permeability is studied using scenario simulation. Third, the ELCC of CWSS under diverse capacity combinations of wind power and energy storage is analyzed based on scenario simulation. Also, the superiority of IPSO is verified by comparing the results of ELCC evaluation with secant method and non-iterative smoothing spline (NISS). The simulation results show that, 1) ELCC of wind power increases with the augment of wind power permeability but finally stabilizes when wind power permeability is 60%. 2) When the permeability of wind power is constant, ELCC of CWSS increases and then does not change basically with the addition of the maximum capacity of energy storage. 3) The permeability of wind power and the maximum capacity of energy storage augment simultaneously will not increase ELCC of CWSS indefinitely.

Suggested Citation

  • Wen, Lei & Song, Qianqian, 2023. "ELCC-based capacity value estimation of combined wind - storage system using IPSO algorithm," Energy, Elsevier, vol. 263(PB).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pb:s0360544222026706
    DOI: 10.1016/j.energy.2022.125784
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222026706
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.125784?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. Behrang, M.A. & Assareh, E. & Noghrehabadi, A.R. & Ghanbarzadeh, A., 2011. "New sunshine-based models for predicting global solar radiation using PSO (particle swarm optimization) technique," Energy, Elsevier, vol. 36(5), pages 3036-3049.
    2. Li, Yuchun & Wang, Jinkuan & Zhang, Yan & Han, Yinghua, 2022. "Day-ahead scheduling strategy for integrated heating and power system with high wind power penetration and integrated demand response: A hybrid stochastic/interval approach," Energy, Elsevier, vol. 253(C).
    3. Xu, Lei & Hou, Lei & Zhu, Zhenyu & Li, Yu & Liu, Jiaquan & Lei, Ting & Wu, Xingguang, 2021. "Mid-term prediction of electrical energy consumption for crude oil pipelines using a hybrid algorithm of support vector machine and genetic algorithm," Energy, Elsevier, vol. 222(C).
    4. Tapetado, Pablo & Usaola, Julio, 2019. "Capacity credits of wind and solar generation: The Spanish case," Renewable Energy, Elsevier, vol. 143(C), pages 164-175.
    5. Xu, Tingting & Gao, Weijun & Qian, Fanyue & Li, Yanxue, 2022. "The implementation limitation of variable renewable energies and its impacts on the public power grid," Energy, Elsevier, vol. 239(PA).
    6. Mills, Andrew D. & Rodriguez, Pía, 2020. "A simple and fast algorithm for estimating the capacity credit of solar and storage," Energy, Elsevier, vol. 210(C).
    7. Zeng, Bo & Wei, Xuan & Zhao, Dongbo & Singh, Chanan & Zhang, Jianhua, 2018. "Hybrid probabilistic-possibilistic approach for capacity credit evaluation of demand response considering both exogenous and endogenous uncertainties," Applied Energy, Elsevier, vol. 229(C), pages 186-200.
    8. Hossain, Md Alamgir & Pota, Hemanshu Roy & Squartini, Stefano & Abdou, Ahmed Fathi, 2019. "Modified PSO algorithm for real-time energy management in grid-connected microgrids," Renewable Energy, Elsevier, vol. 136(C), pages 746-757.
    9. Sodano, Daniel & DeCarolis, Joseph F. & Rodrigo de Queiroz, Anderson & Johnson, Jeremiah X., 2021. "The symbiotic relationship of solar power and energy storage in providing capacity value," Renewable Energy, Elsevier, vol. 177(C), pages 823-832.
    10. Guo, Qiuyi & Zhao, Zhiguo & Shen, Peihong & Zhan, Xiaowen & Li, Jingwei, 2019. "Adaptive optimal control based on driving style recognition for plug-in hybrid electric vehicle," Energy, Elsevier, vol. 186(C).
    11. Gao, Jianwei & Ma, Zeyang & Guo, Fengjia, 2019. "The influence of demand response on wind-integrated power system considering participation of the demand side," Energy, Elsevier, vol. 178(C), pages 723-738.
    12. Zhang, Caiqing & Chen, Panyu, 2022. "Applying the three-stage SBM-DEA model to evaluate energy efficiency and impact factors in RCEP countries," Energy, Elsevier, vol. 241(C).
    13. Erdem, Ergin & Shi, Jing, 2011. "ARMA based approaches for forecasting the tuple of wind speed and direction," Applied Energy, Elsevier, vol. 88(4), pages 1405-1414, April.
    14. Martínez – Lucas, Guillermo & Sarasua, José Ignacio & Fernández – Guillamón, Ana & Molina – García, Ángel, 2021. "Combined hydro-wind frequency control scheme: Modal analysis and isolated power system case example," Renewable Energy, Elsevier, vol. 180(C), pages 1056-1072.
    15. Mukhopadhyay, Bineeta & Das, Debapriya, 2020. "Multi-objective dynamic and static reconfiguration with optimized allocation of PV-DG and battery energy storage system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    16. Yáñez, Juan Pablo & Kunith, Alexander & Chávez-Arroyo, Roberto & Romo-Perea, Alejandro & Probst, Oliver, 2014. "Assessment of the capacity credit of wind power in Mexico," Renewable Energy, Elsevier, vol. 72(C), pages 62-78.
    17. Yin, WanJun & Ming, ZhengFeng & Wen, Tao, 2021. "Scheduling strategy of electric vehicle charging considering different requirements of grid and users," Energy, Elsevier, vol. 232(C).
    18. Stoppato, Anna & Cavazzini, Giovanna & Ardizzon, Guido & Rossetti, Antonio, 2014. "A PSO (particle swarm optimization)-based model for the optimal management of a small PV(Photovoltaic)-pump hydro energy storage in a rural dry area," Energy, Elsevier, vol. 76(C), pages 168-174.
    19. Park, Jungsoo & Lee, Kyo Seung & Kim, Min Su & Jung, Dohoy, 2014. "Numerical analysis of a dual-fueled CI (compression ignition) engine using Latin hypercube sampling and multi-objective Pareto optimization," Energy, Elsevier, vol. 70(C), pages 278-287.
    20. Wilton, Edgar & Delarue, Erik & D’haeseleer, William & van Sark, Wilfried, 2014. "Reconsidering the capacity credit of wind power: Application of cumulative prospect theory," Renewable Energy, Elsevier, vol. 68(C), pages 752-760.
    21. Zheng, Huanyu & Song, Malin & Shen, Zhiyang, 2021. "The evolution of renewable energy and its impact on carbon reduction in China," Energy, Elsevier, vol. 237(C).
    22. Al-Bahrani, Loau Tawfak & Chandra Patra, Jagdish, 2018. "Multi-gradient PSO algorithm for optimization of multimodal, discontinuous and non-convex fuel cost function of thermal generating units under various power constraints in smart power grid," Energy, Elsevier, vol. 147(C), pages 1070-1091.
    23. Fattori, Fabrizio & Anglani, Norma & Staffell, Iain & Pfenninger, Stefan, 2017. "High solar photovoltaic penetration in the absence of substantial wind capacity: Storage requirements and effects on capacity adequacy," Energy, Elsevier, vol. 137(C), pages 193-208.
    24. Zhou, Ella & Cole, Wesley & Frew, Bethany, 2018. "Valuing variable renewable energy for peak demand requirements," Energy, Elsevier, vol. 165(PA), pages 499-511.
    25. Galindo Noguera, Ana Lisbeth & Mendoza Castellanos, Luis Sebastian & Silva Lora, Electo Eduardo & Melian Cobas, Vladimir Rafael, 2018. "Optimum design of a hybrid diesel-ORC / photovoltaic system using PSO: Case study for the city of Cujubim, Brazil," Energy, Elsevier, vol. 142(C), pages 33-45.
    26. Askarzadeh, Alireza, 2014. "Comparison of particle swarm optimization and other metaheuristics on electricity demand estimation: A case study of Iran," Energy, Elsevier, vol. 72(C), pages 484-491.
    27. Shittu, Abdulhakim Adeoye & Mehmanparast, Ali & Hart, Phil & Kolios, Athanasios, 2021. "Comparative study between S-N and fracture mechanics approach on reliability assessment of offshore wind turbine jacket foundations," Reliability Engineering and System Safety, Elsevier, vol. 215(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. Aagaard, Todd & Kleit, Andrew N., 2023. "Marginal vs. average effective load carrying capability: How should capacity markets deal with alternative generation forms?," Utilities Policy, Elsevier, vol. 84(C).
    2. Wang, Renshun & Wang, Shilong & Geng, Guangchao & Jiang, Quanyuan, 2024. "Multi-time-scale capacity credit assessment of renewable and energy storage considering complex operational time series," Applied Energy, Elsevier, vol. 355(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. Wang, Renshun & Wang, Shilong & Geng, Guangchao & Jiang, Quanyuan, 2024. "Multi-time-scale capacity credit assessment of renewable and energy storage considering complex operational time series," Applied Energy, Elsevier, vol. 355(C).
    2. Xu, Tingting & Gao, Weijun & Qian, Fanyue & Li, Yanxue, 2022. "The implementation limitation of variable renewable energies and its impacts on the public power grid," Energy, Elsevier, vol. 239(PA).
    3. Nolting, Lars & Praktiknjo, Aaron, 2022. "The complexity dilemma – Insights from security of electricity supply assessments," Energy, Elsevier, vol. 241(C).
    4. Chen, Tao & Pipattanasomporn, Manisa & Rahman, Imran & Jing, Zejia & Rahman, Saifur, 2020. "MATPLAN: A probability-based planning tool for cost-effective grid integration of renewable energy," Renewable Energy, Elsevier, vol. 156(C), pages 1089-1099.
    5. Huclin, Sébastien & Ramos, Andrés & Chaves, José Pablo & Matanza, Javier & González-Eguino, Mikel, 2023. "A methodological approach for assessing flexibility and capacity value in renewable-dominated power systems: A Spanish case study in 2030," Energy, Elsevier, vol. 285(C).
    6. Zhou, Ella & Cole, Wesley & Frew, Bethany, 2018. "Valuing variable renewable energy for peak demand requirements," Energy, Elsevier, vol. 165(PA), pages 499-511.
    7. Mahmoud M. Gamil & Soichirou Ueda & Akito Nakadomari & Keifa Vamba Konneh & Tomonobu Senjyu & Ashraf M. Hemeida & Mohammed Elsayed Lotfy, 2022. "Optimal Multi-Objective Power Scheduling of a Residential Microgrid Considering Renewable Sources and Demand Response Technique," Sustainability, MDPI, vol. 14(21), pages 1-20, October.
    8. Jia Liang & Yongpei Wang, 2024. "Recognizing the nexus between grid infrastructure, renewable energy, net interregional transmission and carbon emissions: Evidence from China," Growth and Change, Wiley Blackwell, vol. 55(1), March.
    9. Chen, Jiahao & Sun, Bing & Li, Yunfei & Jing, Ruipeng & Zeng, Yuan & Li, Minghao, 2022. "Credible capacity calculation method of distributed generation based on equal power supply reliability criterion," Renewable Energy, Elsevier, vol. 201(P1), pages 534-547.
    10. Mills, Andrew D. & Rodriguez, Pía, 2020. "A simple and fast algorithm for estimating the capacity credit of solar and storage," Energy, Elsevier, vol. 210(C).
    11. Papadimitrakis, M. & Giamarelos, N. & Stogiannos, M. & Zois, E.N. & Livanos, N.A.-I. & Alexandridis, A., 2021. "Metaheuristic search in smart grid: A review with emphasis on planning, scheduling and power flow optimization applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    12. Xu, Xiao & Hu, Weihao & Cao, Di & Huang, Qi & Chen, Cong & Chen, Zhe, 2020. "Optimized sizing of a standalone PV-wind-hydropower station with pumped-storage installation hybrid energy system," Renewable Energy, Elsevier, vol. 147(P1), pages 1418-1431.
    13. AL-Rasheedi, Majed & Al-Khayat, Mohammad, 2024. "Variable renewable energy modeling system to study challenges that impact electrical load at different penetration levels: A case study on Kuwait's load profile," Renewable and Sustainable Energy Reviews, Elsevier, vol. 197(C).
    14. Li, Yanxue & Zhang, Xiaoyi & Gao, Weijun & Ruan, Yingjun, 2020. "Capacity credit and market value analysis of photovoltaic integration considering grid flexibility requirements," Renewable Energy, Elsevier, vol. 159(C), pages 908-919.
    15. Du, Wenyi & Ma, Juan & Yin, Wanjun, 2023. "Orderly charging strategy of electric vehicle based on improved PSO algorithm," Energy, Elsevier, vol. 271(C).
    16. Li, Min & Yang, Yi & He, Zhaoshuang & Guo, Xinbo & Zhang, Ruisheng & Huang, Bingqing, 2023. "A wind speed forecasting model based on multi-objective algorithm and interpretability learning," Energy, Elsevier, vol. 269(C).
    17. Ning Xiang & Limao Wang & Shuai Zhong & Chen Zheng & Bo Wang & Qiushi Qu, 2021. "How Does the World View China’s Carbon Policy? A Sentiment Analysis on Twitter Data," Energies, MDPI, vol. 14(22), pages 1-17, November.
    18. Kandpal, Bakul & Pareek, Parikshit & Verma, Ashu, 2022. "A robust day-ahead scheduling strategy for EV charging stations in unbalanced distribution grid," Energy, Elsevier, vol. 249(C).
    19. Md Jahidur Rahman & Tahar Tafticht & Mamadou Lamine Doumbia & Iqbal Messaïf, 2023. "Optimal Inverter Control Strategies for a PV Power Generation with Battery Storage System in Microgrid," Energies, MDPI, vol. 16(10), pages 1-36, May.
    20. Mousavi, Navid & Kothapalli, Ganesh & Habibi, Daryoush & Das, Choton K. & Baniasadi, Ali, 2020. "A novel photovoltaic-pumped hydro storage microgrid applicable to rural areas," Applied Energy, Elsevier, vol. 262(C).

    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:263:y:2023:i:pb:s0360544222026706. 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.