Ling Ji

An Air Quality Management Model Based on an Interval Dual Stochastic-Mixed Integer Programming

The issue of air pollution has become the focus of the world because of its significant influence to the economic development and public health. This paper proposes an interval dual stochastic-mixed integer programming (IDSIP) approach for regional air quality management. The IDSIP approach can be effectively communicated into the optimization processes and resulting solutions, which is formulated through integrating interval-parameter integer programming (IIP) within a two-stage stochastic programming (TSP) joint chance-constrained programming (CCP) and could deal with uncertainties expressed as not only probability distributions but also interval values. Moreover, the left-hand-side (LHS) constraints with stochastic variables could be handled at different risk levels with varied reliability scenarios. In the modeling formulation, penalties are imposed when expected policies are violated. The results indicate that reasonable solutions for air quality management system have been generated, which can help decision makers draw up productive strategies taking into account the trade-off between system economy and air quality under uncertainty.

Urban water resources allocation under the uncertainties of water supply and demand: a case study of Urumqi, China

In recent years, Urumqi has been suffering from growing water resources shortage due to climate change and socioeconomic activities. Integrated water resources management approach is urgent for regional sustainable development. However, uncertainties around the supply and demand side are problematic for regional water resources planning and policy-making. To address these uncertainties, an inexact multi-stage dual-stochastic programming (IMDSP) was proposed for supporting urban water resources management in Urumqi, China. The developed model can manage the uncertainties of parameters and stochastic variables through the incorporation of interval-parameter programming (IPP), dual-stochastic programming (DSP) and multi-stage optimization programming (MSP). Strategic water-allocation plans that combine surface water, groundwater, diverse water users, and water sources were obtained through the solutions of this model. Varied benefits and recourse costs, water supply risk analysis were conducted in detailed computational results. The obtained results can help decision makers identify optimized water-allocation schemes under water scarcity and environment deterioration.

Environmental and Economic Optimization Model for Electric System Planning in Ningxia, China: Inexact Stochastic Risk-Aversion Programming Approach

The main goal of this paper is to provide a novel risk aversion model for long-term electric power system planning from the manager’s perspective with the consideration of various uncertainties. In the proposed method, interval parameter programming and two-stage stochastic programming are integrated to deal with the technical, economics, and policy uncertainties. Moreover, downside risk theory is introduced to balance the trade-off between the profit and risk according to the decision-maker’s risk aversion attitude. To verify the effectiveness and practical application of this approach, an inexact stochastic risk aversion model is developed for regional electric system planning and management in Ningxia Hui Autonomous Region, China. The series of solutions provide the decision-maker with the optimal investment strategy and operation management under different future emission reduction scenarios and risk-aversion levels. The results indicated that pollution control devices are still the main measures to achieve the current mitigation goal and the adjustment of generation structure would play an important role in the future cleaner electricity system with the stricter environmental policy. In addition, the model can be used for generating decision alternatives and helping decision-makers identify desired energy structure adjustment and pollutants/carbon mitigation abatement policies under various economic and system-reliability constraints.

An inexact two-stage dynamic stochastic model for regional electricity and heat supply management with pollutants mitigation control

BackgroundEnergy system management is an important tool for regional energy and environmental development, and many parameters and their interrelationships in energy-environmental management model appear complexity and uncertain. How to deal with these uncertainties and make a reasonable decision schemes are desired for managers.ResultsIn this study, an inexact two-stage dynamic programming model is developed for regional electricity and heat supply management under considering the complexities and uncertainties in regional energy system. The model can reflect not only uncertainties expressed as probability distribution but also those being available as intervals. The developed model is applied to a case of planning regional electricity and heat supply as well as pollution emission reduction considered.ConclusionsA number of scenarios corresponding to different pollutants emission reduction levels are examined; the results indicated that reasonable solutions have been generated under different pollutants reduction levels. They can be used for generating plans for energy resource/electricity/heat allocation and capacity expansion and help decision makers identify desired regional electricity and heat supply which need minimum cost under various standards of pollutants emission reduction control.