• Energy Research

Abstract In this study, a feasibility based inexact fuzzy programming approach (FBIFP) was developed and applied to the planning of a regional electric power generation system (REPGS). As an extension of existing interval linear programming and fuzzy linear programming, FBIFP could tackle uncertainties expressed as intervals, fuzzy sets as well as their combination in both of the objective function and constraints. Through introducing the index of feasibility degree, FBIFP could be used to facilitate the efficient reflection of constraint violation, and thus allow decision makers to have a complete view of the relationships between uncertain inputs and the related solutions. A hypothetic regional electric power generation system was provided for demonstrating applicability of the developed model. Useful solutions for the planning of REPGS were generated. Interval solutions under different feasibility degrees were obtained. They could be used for helping decision makers identify desired alternatives under various reliability risks of the system. The solutions could also help identify optimal patterns for energy resources allocation, electricity generation and facility capacity expansion with a minimized system cost. Tradeoffs between system costs and system security could be successfully addressed through the analysis of the feasibility degrees of constraints, i.e., a higher feasibility degree would correspond to a higher system cost, while a lower system cost would run into a higher risk of potential instability of the study system.

In this study, a multistage interval-stochastic regional-scale energy model (MIS-REM) is developed for supporting electric power system (EPS) planning under uncertainty that is based on a multistage interval-stochastic integer linear programming method. The developed MIS-REM can deal with uncertainties expressed as both probability distributions and interval values existing in energy system planning problems. Moreover, it can reflect dynamic decisions for electricity generation schemes and capacity expansions through transactions at discrete points of a multiple representative scenario set over a multistage context. It can also analyze various energy-policy scenarios that are associated with economic penalties when the regulated targets are violated. A case study is provided for demonstrating the applicability of the developed model, where renewable and non-renewable energy resources, economic concerns, and environmental requirements are integrated into a systematic optimization process. The results obtained are helpful for supporting (a) adjustment or justification of allocation patterns of regional energy resources and services, (b) formulation of local policies regarding energy consumption, economic development, and energy structure, and (c) analysis of interactions among economic cost, environmental requirement, and energy-supply security.

A kind of agricultural waste, the byproduct of brown-rot fungus Lentinus edodes, was used as an efficient biosorbent for the removal of cadmium from water in this paper. The sorption conditions, such as pH, the dose of biomass and the initial concentration of cadmium were examined. Three kinds of adsorption models were applied to simulate the biosorption data. Uptake of cadmium was higher in weak acid condition than in strong acid condition. Nearly no sorption of cadmium occurred when the pH value was lower than 2.5. Biosorption isothermal data could be well simulated by Freundlich model, and then Langmuir and Temkin model. Langmuir simulation of the biosorption showed that the maximum uptake of cadmium was 5.58mmol/g in weak acid condition, which was much higher than many other biosorbents. The exchanged proton was highly related to the uptake of cadmium in weak acid condition. Fourier transform infrared spectrums and energy-dispersive X-ray microanalyzer were used to reveal ion-exchange mechanism between cadmium and the functional groups or participated inorganic metal ions during biosorption.

Study region: Syr Darya watershed, Central Asia. Study focus: Climate change has the potential to significantly impact the precipitation patterns, available water resources, food security, and ecosystem balance of a watershed. However, limited understanding exists of the temperature, precipitation, and streamflow trends in the Syr Darya watershed under the influence of climate change due to the uncertainties associated with climate change and the incompleteness of observational data. In this study, a multi-GCMs based statistical ensemble analysis (MGSEA) method is developed to effectively reflect the uncertainty of climate predictions and comprehensively assess the impact of climate change on the Syr Darya watershed during the period from 2021 to 2100. New hydrological insights for the region: The variations in temperature, precipitation, and streamflow were assessed by analyzing the projection results of various scenario combinations. Compared to the baseline (1960–2005), the findings reveal a rise in the annual average temperature ranging from 0.2 to 3.8 ℃ for RCP 4.5 and from 1.4 to 5.5 ℃ for RCP 8.5 in the 2080s. Additionally, the research confirms a downward trend in annual precipitation, with decreases of 3.7–27.8% for RCP 4.5 and 5.1–47.7% for RCP 8.5. The results of streamflow analysis exhibit an increasing trend during winter and autumn and a decreasing trend during summer and spring. The research outcomes obtained from MGSEA can be utilized for supporting water resources planning and management.

In this research, a multistagedistribution-generation planning (MDGP) model is developed for clean power generation in the regional distributed generation (DG) power system under multiple uncertainties. The developed model has been applied for sustainable energy system management at Urumqi, China. Various scenarios are designed to reflect variations indemand modes of districts, seasonal limits, potentials of energy replacement, and clean power generation. The model can provide an effective linkage between economic cost and stability of DG power systems. Different power generation schemes would be obtained under different seasonal scenarios and system-failure risk levels. On the other hand, net system costs would be obtained and analyzed. The results indicate that the traditional power generation can be replaced by renewable energy power in DG power systems to satisfy the environmental requestsofthe city of Urumqi. The obtained solutions can help decision-makers get feasible decision alternatives to improve clean power planning in the Urumqi area under various uncertainties.

Abstract Since carbon dioxide (CO 2 ) emission reduction gains global concerns, power industry is one of the main sources of CO 2 emission. Thus, carbon cap and trade scheme and the application of carbon capture and storage have been employed to mitigate CO 2 emission. In consideration of these technologies and constraints, an inexact risk-aversion model based on interval two-stage stochastic programming (ITSP) and conditional value at risk method (CVaR) for multi-period electric system planning problem was developed in this study. Under the optimization framework, more realistic problems such as the demand growth, technology development and environmental policy changes were taken into consideration. CVaR was employed as a risk aversion criterion to reflect the decision maker’s risk preference. A case study for regional electric system planning was demonstrated to verify the capabilities of this optimal approach. The series results provided decision makers with trade-off between the expected system cost and CVaR. It provided valuable insights to make informed long term electric system planning decisions with respect to CCS technology and political and market uncertainties.

Abstract In this study, an interval-robust nonlinear optimization (IRNO) method is developed for planning energy system and managing CO2 emissions with trading scheme, through incorporating interval-parameter programming (IPP) within a robust optimization (RO) framework. In the modeling formulation, two recourse actions were adopted to make the model robustness. One of recourse actions was launched to capture the notion of risk in stochastic programming. The other recourse action was seized the risk of shortage electricity amount during the energy system programming process, which successfully emphasizing the safety of energy system under high-variability. The IRNO method is applied to a case of planning energy system with considering the CO2 emissions management. A number of solutions under different robustness levels have been generated. The results obtained can help generate desired decision alternatives that will be able to enhance energy supply safety with a low system-failure risk level and particularly useful for risk-aversive decision makers in handling high-variability conditions.