Abstract
In this study, a fuzzy bi-level programming (FBP) method is developed for planning water-food-energy (WFE) nexus system. FBP can tackle fuzzy uncertainty expressed as flexible variable and balance the conflict existed in different decision levels. Then, an FBP-WFE model is formulated for Amu Darya River Basin (ARB), in which two-level stakeholders are involved. The upper-level decision maker aims to maximize system benefit, and the lower-level decision maker aims to maximize food production. Three countries, fourteen states, fifteen water users and six planning periods (2021–2050) are also involved in the developed model. Major findings are (i) agricultural sector is the largest water user in ARB (accounting for 78.2%), implying that improving irrigation efficiency and reducing agricultural water allocation are crucial for alleviating water scarcity problem; (ii) there is competition for water resources between electricity generation and food production, with the increasing electricity demand level, the food production would decrease by 2.6‰. The obtained results can provide policy supports for decision makers to alleviate water shortage, ensure food security and energy supply security.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lv, J., Li, Y.P., Shan, B.G., Jin, S.W., Suo, C.: Planning energy-water nexus system under multiple uncertainties – a case study of Hebei province. Appl. Energy 29, 389–403 (2018)
Cai, Y., Rong, Q., Yang, Z., Yue, W., Tan, Q.: An export coefficient based inexact fuzzy bi-level multi-objective programming model for the management of agricultural nonpoint source pollution under uncertainty. J. Hydrol. 557, 713–725 (2018)
Fernandez, E., Hossain, M.J., Mahmud, K., Nizami, M., Kashif, M.: A bi-level optimization-based community energy management system for optimal energy sharing and trading among peers. J. Clean. Prod. 279, 123254 (2021)
Suo, C., Li, Y.P., Sun, J., Yin, S.: An air quality index-based multistage type-2-fuzzy interval-stochastic programming model for energy and environmental systems management under multiple uncertainties. Environ. Res. 167, 98–114 (2018)
Sun, J., Li, Y.P., Suo, C., Liu, Y.R.: Impacts of irrigation efficiency on agricultural water-land nexus system management under multiple uncertainties—a case study in Amu Darya River basin, Central Asia. Agricult. Water Manag. 216, 76–88 (2019)
Jalilov, S.M., Keskinen, M., Varis, O., Amer, S., Ward, F.A.: Managing the water–energy–food nexus: Gains and losses from new water development in Amu Darya River Basin. J. Hydrol. 539, 648–661 (2016)
Acknowledgments
This research was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDA20060302) and the National Key Research and Development Program of China (2016YFC0502803).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix A. Nomenclatures for parameters and variables
Appendix A. Nomenclatures for parameters and variables
i | Different states in Amu Darya River Basin, with i = 1 for Gorno-Badakhshan, i = 2 for Khatlon, i = 3 for RRT, i = 4 for Surkhnadarya, i = 5 for Kashkadarya, i = 6 for Samarkand, i = 7 for Navoi, i = 8 for Bukhara, i = 9 for Khorezm, i = 10 for Karakalpakstan, i = 11 for Mary, i = 12 for Ahal, i = 13 for Lelap, i = 14 for Dashaguz |
j | Water users, with j = 1 for cotton, j = 2 for forage crop, j = 3 for orchard, j = 4 for wheat, j = 5 for corn, j = 6 for cucurbit, j = 7 for potato, j = 8 for rice, j = 9 for sugar beet, j = 10 for vegetable, j = 11 for animal husbandry, j = 12 for domestic, j = 13 for industry, j = 14 for ecology |
t | Planning periods (2021–2050), t = 1, 2, …, 6 |
\(BW_{ijt}^{{}}\) | Net benefit for water user j in state i under planning period t (US$/m3) |
\(AW_{ijt}^{{}}\) | Water allocation for user j in state i under planning period t (m3) |
\(CE_{it}^{{}}\) | Unit cost for water transport for state i under planning period t (US$/m3) |
\(PSE_{ijt}^{{}}\) | Electricity consumed for pumping water (KWh/m3) |
\(TSE_{ijt}^{{}}\) | Electricity consumed for transporting water (KWh/ m3) |
\(UE_{ijt}^{{}}\) | Electricity consumed for distributing water (KWh/ m3) |
\(\gamma_{ijt}^{{}}\) | Water allocation efficiency |
\(QF_{ijt}^{{}}\) | The amount of fertilizer consumed by user j in state i under planning period t (kg/ha) |
\(PF_{ijt}^{{}}\) | Unit cost for fertilizer for user j in state i under planning period t (US$/kg) |
\(WPC_{ijt}^{{}}\) | Water requirement per unit planting area (m3/ha) |
\(FC_{ijt}^{{}}\) | Fixed cost for agricultural planting for user j in state i under planning period t (US$/ha) |
\(BEW_{ijt}^{{}}\) | Net benefit per unit of electricity generation (US$/KWh) |
\(EW_{it}^{{}}\) | Water allocated for electricity generation (m3) |
\(PTH_{it}^{{}}\) | Water allocation ratio for thermal power generation (%) |
\(\varphi_{THit}\) | Coefficient of water consumption in thermal power generation (KWh/m3) |
\(\eta_{t}\) | Electricity transmission loss ratio (%) |
\(PTH_{it}^{{}}\) | Water allocation ratio for thermal power generation (%) |
\(\varphi_{HYit}\) | Coefficient of water consumption in hydropower generation (KWh/m3) |
\(PHY_{it}^{{}}\) | Water allocation ratio for hydropower generation (%) |
\(TAA_{t}\) | The total volume of water resources in period t (m3) |
\(WAL_{t}^{{}}\) | Water loss in channel of Amu Darya river in period t (m3) |
\(YA_{ijt}\) | Crop output per area for user j in state i under planning period t (kg/ha) |
\(TW_{ijt}\) | Water demand for user j in state i under planning period t (m3) |
\(IOE_{t}\) | The amount of imported electricity (KWh) |
\(ELD_{t}\) | The total amount of electricity demand (KWh) |
\(TAED_{t}\) | The amount of electricity demand for Tajikistan (KWh) |
\(UZED_{t}\) | The amount of electricity demand for Uzbekistan (KWh) |
\(TUED_{t}\) | The amount of electricity demand for Turkmenistan (KWh) |
\(ARE_{t}^{\min }\) | The minimum area of cultivated land (ha) |
\(ARE_{t}^{\max }\) | The maximum area of cultivated land (ha) |
\(ALE_{t}\) | The amount of electricity consumed during water allocation (KWh) |
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ma, Y., Li, Y.P., Huang, G.H. (2022). An Integrated Bi-level Optimization Model for Planning Water-Food-Energy Nexus System Under Uncertainty. In: Ujikawa, K., Ishiwatari, M., Hullebusch, E.v. (eds) Environment and Sustainable Development. ACESD 2021. Environmental Science and Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-19-1704-2_28
Download citation
DOI: https://doi.org/10.1007/978-981-19-1704-2_28
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-1703-5
Online ISBN: 978-981-19-1704-2
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)