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A two-stage mathematical programming model for distributed photovoltaic project portfolio selection with incomplete preference information

Abstract

With the rapid growth of the solar photovoltaic (PV) market, many distributed PV power projects are introduced to the market. Selecting a rational project investment portfolio is a complex and challenging task for energy enterprises as both financial and non-financial factors of projects are needed to be considered under limited information and resources. This study presents a two-stage hybrid multi-attribute decision-making and integer programming model for distributed PV project portfolio selection. In Stage I, a multiple attribute group decision-making method based on mathematical programming is used to evaluate the non-financial value of projects under incomplete preference information. Compensative weighted averaging operators with an adjustable parameter are utilized to capture the subjective attitudinal character of an expert in the aggregation process. Then, a rank acceptability index is developed to measure each project’s group support degree in non-financial dimension. In Stage II, a bi-objective integer programming model is constructed to optimize project portfolios, which considers both financial and non-financial values of projects under resource, carbon emission and other strategic constraints. The applicability and effectivity of the proposed approach are demonstrated by a case study of a distributed PV project portfolio selection.

Keyword : distributed photovoltaic, project portfolio, multiple attribute group decision making, incomplete preference information

How to Cite
Zhang, Z., & Liao, H. (2022). A two-stage mathematical programming model for distributed photovoltaic project portfolio selection with incomplete preference information. Technological and Economic Development of Economy, 28(5), 1545–1571. https://doi.org/10.3846/tede.2022.17683
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Oct 10, 2022
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References

Aggarwal, M. (2015). Compensative weighted averaging aggregation operators. Applied Soft Computing, 28, 368–378. https://doi.org/10.1016/j.asoc.2014.09.049

Aggarwal, M., & Fallah Tehrani, A. (2019). Modelling human decision behaviour with preference learning. INFORMS Journal on Computing, 31(2), 318–334. https://doi.org/10.1287/ijoc.2018.0823

Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197. https://doi.org/10.1109/4235.996017

Fang, H., Li, J., & Song, W. Y. (2018). Sustainable site selection for photovoltaic power plant: An integrated approach based on prospect theory. Energy Conversion and Management, 174, 755–768. https://doi.org/10.1016/j.enconman.2018.08.092

Gao, J. W., Guo, F. J., Ma, Z. Y., & Huang, X. (2021). Multi-criteria decision-making framework for large-scale rooftop photovoltaic project site selection based on intuitionistic fuzzy sets. Applied Soft Computing, 102, 107098. https://doi.org/10.1016/j.asoc.2021.107098

Goli, A., Khademi Zare, H., Tavakkoli-Moghaddam, R., & Sadeghieh, A. (2019). Hybrid artificial intelligence and robust optimization for a multi-objective product portfolio problem Case study: The dairy products industry. Computers & Industrial Engineering, 137, 106090. https://doi.org/10.1016/j.cie.2019.106090

Goli, A., & Mohammadi, H. (2022). Developing a sustainable operational management system using hybrid Shapley value and Multimoora method: case study petrochemical supply chain. Environment, Development and Sustainability, 24, 10540–10569. https://doi.org/10.1007/s10668-021-01844-9

Kannan, D., Moazzeni, S., Darmian, S. M., & Afrasiabi, A. (2021). A hybrid approach based on MCDM methods and Monte Carlo simulation for sustainable evaluation of potential solar sites in east of Iran. Journal of Cleaner Production, 279, 122368. https://doi.org/10.1016/j.jclepro.2020.122368

Li, D. F., & Wan, S. P. (2013). Fuzzy linear programming approach to multiattribute decision making with multiple types of attribute values and incomplete weight information. Applied Soft Computing, 13(11), 4333–4348. https://doi.org/10.1016/j.asoc.2013.06.019

Liang, Y. Y., Ju, Y. B., Qin, J. D., & Pedrycz, W. (2021). Multi-granular linguistic distribution evidential reasoning method for renewable energy project risk assessment. Information Fusion, 65, 147–164. https://doi.org/10.1016/j.inffus.2020.08.010

Liao, H. C., Xu, Z. S., Zeng, X. J., & Merigó, J. M. (2015). Qualitative decision making with correlation coefficients of hesitant fuzzy linguistic term sets. Knowledge-Based Systems, 76, 127–138. https://doi.org/10.1016/j.knosys.2014.12.009

Liu, A. H., Wan, S. P., & Dong, J. Y. (2021). An axiomatic design-based mathematical programming method for heterogeneous multi-criteria group decision making with linguistic fuzzy truth degrees. Information Sciences, 571, 649–675. https://doi.org/10.1016/j.ins.2021.04.091

Ozdemir, S., & Sahin, G. (2018). Multi-criteria decision-making in the location selection for a solar PV power plant using AHP. Measurement, 129, 218–226. https://doi.org/10.1016/j.measurement.2018.07.020

Rediske, G., Siluk, J. C. M., Michels, L., Rigo, P. D., Rosa, C. B., & Cugler, G. (2020). Multi-criteria decision-making model for assessment of large photovoltaic farms in Brazil. Energy, 197, 117167. https://doi.org/10.1016/j.energy.2020.117167

Rodríguez, R. M., Martı́nez, L., & Herrera, F. (2012). Hesitant fuzzy linguistic terms sets for decision making. IEEE Transactions on Fuzzy Systems, 20, 109–119. https://doi.org/10.1109/TFUZZ.2011.2170076

Shakouri, M., Lee, H. W., & Choi, K. (2015). PACPIM: New decision-support model of optimized portfolio analysis for community-based photovoltaic investment. Applied Energy, 156, 607–617. https://doi.org/10.1016/j.apenergy.2015.07.060

Song, S. L., Ang, S., Yang, F., & Xia, Q. (2019). An stochastic multiattribute acceptability analysis‐based method for the multiattribute project portfolio selection problem with rank‐level information. Expert Systems, 36(5), e12447. https://doi.org/10.1111/exsy.12447

Srinivasan, V., & Shocker, A. (1973). Linear programming techniques for multidimensional analysis of preference. Psychometrica, 38, 337–367. https://doi.org/10.1007/BF02291658

Sward, J. A., Nilson, R. S., Katkar, V. V., Stedman, R. C., Kay, D. L., Ifft, J. E., & Zhang, K. M. (2021). Integrating social considerations in multicriteria decision analysis for utility-scale solar photovoltaic siting. Applied Energy, 288, 116543. https://doi.org/10.1016/j.apenergy.2021.116543

Tirkolaee, E. B., Goli, A., Ghasemi, P., & Goodarzian, F. (2022). Designing a sustainable closed-loop supply chain network of face masks during the COVID-19 pandemic: Pareto-based algorithms. Journal of Cleaner Production, 333, 130056. https://doi.org/10.1016/j.jclepro.2021.130056

Wan, S. P., & Dong, J. Y. (2015). Interval-valued intuitionistic fuzzy mathematical programming method for hybrid multi-criteria group decision making with interval-valued intuitionistic fuzzy truth degrees. Information Fusion, 26, 49–65. https://doi.org/10.1016/j.inffus.2015.01.006

Wan, S. P., & Li, D. F. (2013). Fuzzy LINMAP approach to heterogeneous MADM considering comparisons of alternatives with hesitation degrees. Omega, 41(6), 925–940. https://doi.org/10.1016/j.omega.2012.12.002

Wan, S. P., & Li, D. F. (2014). Atanassov’s intuitionistic fuzzy programming method for heterogeneous multiattribute group decision making with atanassov’s intuitionistic fuzzy truth degrees. IEEE Transactions on Fuzzy Systems, 22(2), 300–312. https://doi.org/10.1109/TFUZZ.2013.2253107

Wan, S. P., & Li, D. F. (2015). Fuzzy mathematical programming approach to heterogeneous multiattribute decision-making with interval-valued intuitionistic fuzzy truth degrees. Information Sciences, 325, 484–503. https://doi.org/10.1016/j.ins.2015.07.014

Wan, S. P., Qin, Y. L., & Dong, J. Y. (2017). A hesitant fuzzy mathematical programming method for hybrid multi-criteria group decision making with hesitant fuzzy truth degrees. Knowledge-Based Systems, 138, 232–248. https://doi.org/10.1016/j.knosys.2017.10.002

Wei, Q. S. (2021). Sustainability evaluation of photovoltaic poverty alleviation projects using an integrated MCDM method: A case study in Guangxi, China. Journal of Cleaner Production, 302, 127040. https://doi.org/10.1016/j.jclepro.2021.127040

Wu, Y. N., Wang, J., Ji, S. Y., Song, Z. X., & Ke, Y. M. (2019). Optimal investment selection of industrial and commercial rooftop distributed PV project based on combination weights and cloud-TODIM model from SMEs’ perspectives. Journal of Cleaner Production, 234, 534–548. https://doi.org/10.1016/j.jclepro.2019.06.249

Wu, Y. N., Wu, C. H., Zhou, J. L., He, F. Y., Xu, C. B., Zhang, B. Y., & Zhang, T. (2020). An investment decision framework for photovoltaic power coupling hydrogen storage project based on a mixed evaluation method under intuitionistic fuzzy environment. Journal of Energy Storage, 30, 101601. https://doi.org/10.1016/j.est.2020.101601

Wu, Y. N., Xu, C. B., Ke, Y. M., Chen, K. F., & Sun, X. K. (2018). An intuitionistic fuzzy multi-criteria framework for large-scale rooftop PV project portfolio selection: Case study in Zhejiang, China. Energy, 143, 295–309. https://doi.org/10.1016/j.energy.2017.10.105

Wu, Y. N., Xu, C. B., Ke, Y. M., Li, X. Y., & Li, L. W. Y. (2019). Portfolio selection of distributed energy generation projects considering uncertainty and project interaction under different enterprise strategic scenarios. Applied Energy, 236, 444–464. https://doi.org/10.1016/j.apenergy.2018.12.009

Zambrano-Asanza, S., Quiros-Tortos, J., & Franco, J. F. (2021). Optimal site selection for photovoltaic power plants using a GIS-based multi-criteria decision making and spatial overlay with electric load. Renewable and Sustainable Energy Reviews, 143, 110853. https://doi.org/10.1016/j.rser.2021.110853

Zhang, F., Deng, H., Margolis, R., & Su, J. (2015). Analysis of distributed-generation photovoltaic deployment, installation time and cost, market barriers, and policies in China. Energy Policy, 81, 43–55. https://doi.org/10.1016/j.enpol.2015.02.010

Zhang, M. M., Tang, Y. M., Liu, L. Y., & Zhou, D. Q. (2022). Optimal investment portfolio strategies for power enterprises under multi-policy scenarios of renewable energy. Renewable and Sustainable Energy Reviews, 154, 111879. https://doi.org/10.1016/j.rser.2021.111879

Zuo, W. J., Li, D. F., & Yu, G. F. (2020). A general multi-attribute multi-scale decision making method based on dynamic linmap for property perceived service quality evaluation. Technological and Economic Development of Economy, 26(5), 1052–1073. https://doi.org/10.3846/tede.2020.12726