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A framework for quantifying the value of information to mitigate risk in the optimal design of distributed energy systems under uncertainty

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  • Niu, Jide
  • Li, Xiaoyuan
  • Tian, Zhe
  • Yang, Hongxing

Abstract

Distributed energy systems (DESs) are regarded as promising systems for integrating renewable energy sources. However, uncertainties arising from renewable energy and loads introduce significant complexity to DES design and may even result in reliability and economic risks when the design of DESs relies on limited information. Gathering more information can reduce uncertainty, thereby improving the robustness of the DES scheme. However, obtaining information comes at a cost, and too much information can result in redundant work and unnecessary computing burden. Conversely, discarding or ignoring information may pose risks to reliability and the economy. Therefore, this study presents a framework for quantifying the value of uncertainty information, which can help to understand how information affects risk and identify key information that facilitates DES risk aversion. Two information value indices, namely the expected values of information for reliability (EVPIr) and economy (EVPIe), are developed to measure the risk reduction of reliability and economy when more information is added to the design of DESs. Furthermore, a two-layer information value quantification model based on mixed integer linear programming is built to optimize the design of DESs based on uncertain information and quantify the value of information based on a relatively complete information set. The proposed information value quantification method is tested on a real DES under three types of uncertain design boundary scenarios. The results show that the values of EVPIr and EVPIe decrease with increasing information of uncertain design boundary scenarios, indicating that more information reduces risks. An unexpected discovery is that the probability information of the scenario set is not critical for DESs. The deviations of EVPIe are within ±2%. The proposed approach offers a quantitative means to evaluate and filter key information for planning scenarios, which can facilitate the generation of streamlined planning scenarios without compromising reliability and economy.

Suggested Citation

  • Niu, Jide & Li, Xiaoyuan & Tian, Zhe & Yang, Hongxing, 2023. "A framework for quantifying the value of information to mitigate risk in the optimal design of distributed energy systems under uncertainty," Applied Energy, Elsevier, vol. 350(C).
    • Handle: RePEc:eee:appene:v:350:y:2023:i:c:s0306261923010814
    DOI: 10.1016/j.apenergy.2023.121717
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    as
    1. Niu, Jide & Tian, Zhe & Lu, Yakai & Zhao, Hongfang & Lan, Bo, 2019. "A robust optimization model for designing the building cooling source under cooling load uncertainty," Applied Energy, Elsevier, vol. 241(C), pages 390-403.
    2. Mu, Yunfei & Wang, Congshan & Cao, Yan & Jia, Hongjie & Zhang, Qingzhu & Yu, Xiaodan, 2022. "A CVaR-based risk assessment method for park-level integrated energy system considering the uncertainties and correlation of energy prices," Energy, Elsevier, vol. 247(C).
    3. Yang, Jun & Su, Changqi, 2021. "Robust optimization of microgrid based on renewable distributed power generation and load demand uncertainty," Energy, Elsevier, vol. 223(C).
    4. Perera, A.T.D. & Nik, Vahid M. & Mauree, Dasaraden & Scartezzini, Jean-Louis, 2017. "Electrical hubs: An effective way to integrate non-dispatchable renewable energy sources with minimum impact to the grid," Applied Energy, Elsevier, vol. 190(C), pages 232-248.
    5. Petkov, Ivalin & Gabrielli, Paolo, 2020. "Power-to-hydrogen as seasonal energy storage: an uncertainty analysis for optimal design of low-carbon multi-energy systems," Applied Energy, Elsevier, vol. 274(C).
    6. Zhou, Zhe & Zhang, Jianyun & Liu, Pei & Li, Zheng & Georgiadis, Michael C. & Pistikopoulos, Efstratios N., 2013. "A two-stage stochastic programming model for the optimal design of distributed energy systems," Applied Energy, Elsevier, vol. 103(C), pages 135-144.
    7. Luo, Jianing & Li, Hangxin & Wang, Shengwei, 2022. "A quantitative reliability assessment and risk quantification method for microgrids considering supply and demand uncertainties," Applied Energy, Elsevier, vol. 328(C).
    8. Guo, Li & Hou, Ruosong & Liu, Yixin & Wang, Chengshan & Lu, Hai, 2020. "A novel typical day selection method for the robust planning of stand-alone wind-photovoltaic-diesel-battery microgrid," Applied Energy, Elsevier, vol. 263(C).
    9. Zhang, Bingying & Li, Qiqiang & Wang, Luhao & Feng, Wei, 2018. "Robust optimization for energy transactions in multi-microgrids under uncertainty," Applied Energy, Elsevier, vol. 217(C), pages 346-360.
    10. Gabrielli, Paolo & Fürer, Florian & Mavromatidis, Georgios & Mazzotti, Marco, 2019. "Robust and optimal design of multi-energy systems with seasonal storage through uncertainty analysis," Applied Energy, Elsevier, vol. 238(C), pages 1192-1210.
    11. Ahn, Hyeunguk & Rim, Donghyun & Pavlak, Gregory S. & Freihaut, James D., 2019. "Uncertainty analysis of energy and economic performances of hybrid solar photovoltaic and combined cooling, heating, and power (CCHP + PV) systems using a Monte-Carlo method," Applied Energy, Elsevier, vol. 255(C).
    12. Pilpola, Sannamari & Lund, Peter D., 2020. "Analyzing the effects of uncertainties on the modelling of low-carbon energy system pathways," Energy, Elsevier, vol. 201(C).
    13. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "Comparison of alternative decision-making criteria in a two-stage stochastic program for the design of distributed energy systems under uncertainty," Energy, Elsevier, vol. 156(C), pages 709-724.
    14. Erick Delage & Yinyu Ye, 2010. "Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems," Operations Research, INFORMS, vol. 58(3), pages 595-612, June.
    15. Niu, Jide & Tian, Zhe & Yue, Lu, 2020. "Robust optimal design of building cooling sources considering the uncertainty and cross-correlation of demand and source," Applied Energy, Elsevier, vol. 265(C).
    16. Fu, Xueqian & Sun, Hongbin & Guo, Qinglai & Pan, Zhaoguang & Xiong, Wen & Wang, Li, 2017. "Uncertainty analysis of an integrated energy system based on information theory," Energy, Elsevier, vol. 122(C), pages 649-662.
    17. A. T. D. Perera & Vahid M. Nik & Deliang Chen & Jean-Louis Scartezzini & Tianzhen Hong, 2020. "Quantifying the impacts of climate change and extreme climate events on energy systems," Nature Energy, Nature, vol. 5(2), pages 150-159, February.
    18. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "Design of distributed energy systems under uncertainty: A two-stage stochastic programming approach," Applied Energy, Elsevier, vol. 222(C), pages 932-950.
    19. Da Li & Shijie Zhang & Yunhan Xiao, 2020. "Interval Optimization-Based Optimal Design of Distributed Energy Resource Systems under Uncertainties," Energies, MDPI, vol. 13(13), pages 1-18, July.
    20. Karmellos, M. & Georgiou, P.N. & Mavrotas, G., 2019. "A comparison of methods for the optimal design of Distributed Energy Systems under uncertainty," Energy, Elsevier, vol. 178(C), pages 318-333.
    21. Soroudi, Alireza & Amraee, Turaj, 2013. "Decision making under uncertainty in energy systems: State of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 376-384.
    22. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "Uncertainty and global sensitivity analysis for the optimal design of distributed energy systems," Applied Energy, Elsevier, vol. 214(C), pages 219-238.
    23. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
    24. Zatti, Matteo & Gabba, Marco & Freschini, Marco & Rossi, Michele & Gambarotta, Agostino & Morini, Mirko & Martelli, Emanuele, 2019. "k-MILP: A novel clustering approach to select typical and extreme days for multi-energy systems design optimization," Energy, Elsevier, vol. 181(C), pages 1051-1063.
    25. Yu, Jiah & Ryu, Jun-Hyung & Lee, In-beum, 2019. "A stochastic optimization approach to the design and operation planning of a hybrid renewable energy system," Applied Energy, Elsevier, vol. 247(C), pages 212-220.
    26. Majewski, Dinah Elena & Lampe, Matthias & Voll, Philip & Bardow, André, 2017. "TRusT: A Two-stage Robustness Trade-off approach for the design of decentralized energy supply systems," Energy, Elsevier, vol. 118(C), pages 590-599.
    27. Kuang, Zhonghong & Chen, Qi & Yu, Yang, 2022. "Assessing the CO2-emission risk due to wind-energy uncertainty," Applied Energy, Elsevier, vol. 310(C).
    28. Yang, Dongfeng & Jiang, Chao & Cai, Guowei & Yang, Deyou & Liu, Xiaojun, 2020. "Interval method based optimal planning of multi-energy microgrid with uncertain renewable generation and demand," Applied Energy, Elsevier, vol. 277(C).
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