S. Nie

Development of a Fuzzy-Boundary Interval Programming Method for Water Quality Management Under Uncertainty

This study develops a fuzzy-boundary interval programming (FBIP) method for tackling dual uncertainties expressed as crisp intervals and fuzzy-boundary intervals. An interactive algorithm and a vertex analysis approach are proposed for solving the FBIP model and solutions with α-cut levels have been generated. FBIP is applied to planning water quality management of Xiangxi River in the Three Gorges Reservoir Region, China. Biological oxygen demand (BOD), total nitrogen (TN), and total phosphorus (TP) are selected as water quality indicators to determine the pollution control strategies. Results reveal that the highest discharge of BOD is observed at the Baishahe chemical plant, among all point and nonpoint sources; crop farming is the main nonpoint source with the excessive nitrogen loading due to too much uses of livestock manures and chemical fertilizers; phosphorus discharge derives mainly from point sources (i.e. chemical plants and phosphorus mining companies). Abatement of pollutant discharges from industrial and agricultural activities is critical for the river pollution control; however, the implementation of management practices for pollution control can have potentials to affect the local economic income. These findings can help generate desired decisions for identifying various industrial and agricultural activities in association with both maximizing economic income and mitigating river-water pollution.

An integrated optimization method for river water quality management and risk analysis in a rural system.

In this study, an interval-stochastic-based risk analysis (RSRA) method is developed for supporting river water quality management in a rural system under uncertainty (i.e., uncertainties exist in a number of system components as well as their interrelationships). The RSRA method is effective in risk management and policy analysis, particularly when the inputs (such as allowable pollutant discharge and pollutant discharge rate) are expressed as probability distributions and interval values. Moreover, decision-makers’ attitudes towards system risk can be reflected using a restricted resource measure by controlling the variability of the recourse cost. The RSRA method is then applied to a real case of water quality management in the Heshui River Basin (a rural area of China), where chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), and soil loss are selected as major indicators to identify the water pollution control strategies. Results reveal that uncertainties and risk attitudes have significant effects on both pollutant discharge and system benefit. A high risk measure level can lead to a reduced system benefit; however, this reduction also corresponds to raised system reliability. Results also disclose that (a) agriculture is the dominant contributor to soil loss, TN, and TP loads, and abatement actions should be mainly carried out for paddy and dry farms; (b) livestock husbandry is the main COD discharger, and abatement measures should be mainly conducted for poultry farm; (c) fishery accounts for a high percentage of TN, TP, and COD discharges but a has low percentage of overall net benefit, and it may be beneficial to cease fishery activities in the basin. The findings can facilitate the local authority in identifying desired pollution control strategies with the tradeoff between socioeconomic development and environmental sustainability.

An integrated bi-level optimization model for air quality management of Beijing’s energy system under uncertainty

In this study, an interval chance-constrained bi-level programming (ICBP) method is developed for air quality management of municipal energy system under uncertainty. ICBP can deal with uncertainties presented as interval values and probability distributions as well as examine the risk of violating constraints. Besides, a leader-follower decision strategy is incorporated into the optimization process where two decision makers with different goals and preferences are involved. To solve the proposed model, a bi-level interactive algorithm based on satisfactory degree is introduced into the decision-making processes. Then, an ICBP based energy and environmental systems (ICBP-EES) model is formulated for Beijing, in which air quality index (AQI) is used for evaluating the integrated air quality of multiple pollutants. Result analysis can help different stakeholders adjust their tolerances to achieve the overall satisfaction of EES planning for the study city. Results reveal that natural gas is the main source for electricity-generation and heating that could lead to a potentially increment of imported energy for Beijing in future. Results also disclose that PM10 is the major contributor to AQI. These findings can help decision makers to identify desired alternatives for EES planning and provide useful information for regional air quality management under uncertainty.

Two-stage vertex analysis method for planning electric power systems with greenhouse gas abatement consideration

ABSTRACT This study presents a two-stage vertex analysis (TSVA) method for the planning of electric power systems (EPS) under uncertainty. TSVA has advantages in comparison to other optimization techniques. Firstly, TSVA can incorporate greenhouse gas (GHG) abatement policies directly into its optimization process, and, secondly, it can readily integrate inherent system uncertainties expressed as fuzzy sets and probability distributions directly into its modeling formulation and solution procedure. The TSVA method is applied to a case study of planning EPS and it is demonstrated how the TSVA efficiently identify optimal electricity-generation schemes that could help to minimize system cost under different GHG-abatement considerations. Different combinative considerations on the uncertain inputs lead to varied system costs and GHG emissions. Results reveal that the total electricity supply will rise up along with the time period due to the increasing demand and, at the same time, more non-fossil fuels should be used to satisfy the increasing requirement for GHG mitigation. Moreover, uncertainties in connection with complexities in terms of information quality (e.g., capacity, efficiency, and demand) result in changed electricity-generation patterns, GHG-abatement amounts, as well as system costs. Minimax regret (MMR) analysis technique is employed to identify desired alternative that reflects compromises between system cost and system-failure risk.