Aiming at the bi-level multi-objective characteristic of the energy-environment-economy(3E) system of China, this paper constructed a bi-level multi-objective optimization model. Six scenarios were simulated in prefer...
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Aiming at the bi-level multi-objective characteristic of the energy-environment-economy(3E) system of China, this paper constructed a bi-level multi-objective optimization model. Six scenarios were simulated in preference to energy saving, emission reduction and economic development. And energy consumption, carbon emission and economic development were analyzed in different scenarios during the 13th five-year plan (2016-2020). The following results were obtained: during the 13th five-year plan, the national targets of energy consumption, carbon emission and economic development are easily achievable. However, it is hard for most regions to achieve their energy conservation and emission reduction (ECER) targets when accomplishing their own economic targets. In other words, regional economic targets mismatch their ECER targets. The effects of ECER are not ideal in the energy saving scenario and the carbon emission reduction scenario, while they are relatively satisfactory in the economic development scenario. The win-win" situation between upper-level and lower-level is realizable in the economic development scenario, i.e., high-quality economic development may germinate good effects on ECER.
Because unexpected, high-impact, extreme events and disasters can seriously threaten power system safety and resilience, there has been an increased research focus on power system resilience. This study proposes a bi-...
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Because unexpected, high-impact, extreme events and disasters can seriously threaten power system safety and resilience, there has been an increased research focus on power system resilience. This study proposes a bi-level multi-objective model for industrial park distributed energy configuration optimization to deal with extreme events, which considers the interactions between the authority and the industrial park in a leader-follower decision process and seeks to trade-off between economic cost, environmental protection, and power system resilience. An interactive algorithm is designed and the ������-constraint method is used to convert the model into a bi-level single objective model to solve the proposed complex model. A case example from an industrial park in Wuxi, Jiangsu Province, China, is given to demonstrate the practicality and efficiency of the proposed optimization method. The joint analysis of the power supply and financial losses found that a distributed energy system with an optimal resilience configuration could guarantee 95.85% of demand for three consecutive hours after an extreme event, maintain more than 50% of demand at any time, and reduce power outage financial losses at any time to a minimum of 0.232 million CNY. Based on the analyses and discussion, this model was proven to provide a reasonable and practical strategy for a resilience-economy-environment trade-off in industrial park distributed energy systems.
Maintaining the sustainability of small and medium-sized enterprises (SMEs) supply chain has positive impact on society. Under the ripple effect, the vulnerability of the SMEs supply chain and the collaboration uncert...
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Maintaining the sustainability of small and medium-sized enterprises (SMEs) supply chain has positive impact on society. Under the ripple effect, the vulnerability of the SMEs supply chain and the collaboration uncertainty of participants have potentially negative impact on the sustainability that cannot be ignored. We consider the closed-loop SMEs supply chain network design based on the risk propagation to improve the triple sustainability. The basis of the simulation model structure is a bi-level multi-objective mixed-integer linear programming model that aims to maximise triple sustainability, and an improved dynamic non-dominated genetic algorithm (DNSGA2) is designed to solve the problem. An SMEs supply chain for closed-loop manufacturing of lithium batteries in China was considered. The results show that the centralised SMEs supply chain preform better when the supply-demand imbalance, and the distributed network structure is preform better in maintaining sustainability when manufacturer capacity imbalances. Supply chain managers should develop more flexible strategies based on the structure of downstream demand, increasing upstream manufacturers' participation to enhance their viability and improve the sustainability of the supply chain.
As the global demand for clean energy continues to grow, the sustainable development of clean energy projects has become an important topic of research. in order to optimize the performance and sustainability of clean...
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As the global demand for clean energy continues to grow, the sustainable development of clean energy projects has become an important topic of research. in order to optimize the performance and sustainability of clean energy projects, this work explores the environmental and economic benefits of the clean energy industry. through the use of Support Vector Machine (SVM) multi-factor models and a bi -levelmulti-objective approach, this work conducts comprehensive assessment and optimization. with wind power base a as a case study, the work describes the material consumption of wind turbines, transportation energy consumption an d carbon dioxide (CO2) emissions, and infrastructure material consumption through descriptive statistics. Moreover, this work analyzes the characteristics of different wind turbine models in depth. On one hand, the SVM multi-factor model is used to predict and assess the profitability of Wind Power Base A. On the other hand, a bi -levelmulti-objective approach is applied to optimize the number of units, internal rate of return within the project, and annual average equivalent utilization hours of the Wind Power Base A. The research results indicate that in March, the WilderHill New Energy Global Innovation Index (NEX) was 0.91053, while the predicted value of the SVM multi-factor model was 0.98596. The predicted value is slightly higher than the actual value, demonstrating the model 's good grasp of future returns. The cumulative rate of return of Wind Power Base A is 18.83%, with an annualized return of 9.47%, exceeding the market performance by 1.68%. Under the optimization of the bi -levelmulti-objective approach, the number of units at Wind Power Base A decreases from the original 7004 to 5860, with total purchase and transportation costs remaining basically unchanged. The internal rate of return of the project increases from 8% to 9.3%, and the annual equivalent utilization hours increase to 2044 h, comprehensively improving the investm
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