Models For Industrial Park Water-energy Resources Optimal Allocation Based On Uncertainty Analysis

As an existing form of high aggregation of industries,industrial parks have become the largest energy consumer and the second largest freshwater consumer,while contributing more than 50% of the country’s total industrial output value.The shortage of resources has being increasingly prominent and become the main bottleneck restricting the sustainable development of industrial parks.Moreover,the optimal allocation of resources in industrial parks have multiple uncertainties due to the changeable of natural conditions and the differences of decision maker,which intensify the risk of resources shortage and reduce system economic benefits.The past researches only focused on the impact of single uncertainty within the optimal allocation of system resources,which ignored the impact of multiple uncertainties.This will result in the missing of information and obtain inaccurate results.Under the background of global warming,population increasing and the shortage of resources,how to scientifically and rationally optimize the allocation of limited water and energy resources has become a research hotspot to improve the efficiency of resource utilization and ensure the green and sustainable development of the industrial parks.Based on the natural and social attributes of industrial parks,this thesis proposes water and energy resource management models under multiple uncertainties,respectively.It can quantitatively analyze the loss of system which cause by the uncertainty of resource elements in the industrial park system,and provides decision support for the efficient allocation of industrial park resources.It’s effective to realize the efficient utilization of the basic resources of the industrial park system,and promote the green and low-carbon transformation and long-term development of the industrial park.The main contents and conclusions of this research can be listed as follows:（1）In the industrial park water resources management,an interval two-stage stochastic partial fractional programming model（ITSPF）is proposed,which couples interval linear programming,two-stage stochastic programming,the theory of partial information and fractional programming.It can optimize the proportion problem,while consider the impact of the dual uncertainties of interval probability on the allocation of water resources.The results show that different water distribution schemes can be generated under different probabilities of water flow levels by dealing with the balance between water consumption and system benefits.The use of reclaimed water would also increase while the risk of water scarcity is getting higher.In addition,the comparisons of ITSPF results against the least-cost model and model with deterministic probability of water inflow levels demonstrated that ITSPF could not only result in higher resource-use efficiency,but also avoid missing possible solution sets and offer a pragmatic way for obtaining satisfactory alternatives by providing wider adjustable ranges.（2）In the industrial park energy resource management,the electric power system is the largest energy consumption sector in industrial park system.Previous studies ignored the impacts of uncertainties such as the uncertain probabilities of electricity demand and the fuzziness of renewable energy,which made the results differ greatly from the reality.Thus,an interval two-stage partial fuzzy programming was established by integrating interval two-stage stochastic programming,partial linear theory and fuzzy credibility constraints programming,which was useful to tackle multiple uncertainties.In addition,in order to achieve the low-carbon development of system,a ladder-type carbon trading mechanism was introduced to form ladder-type carbon trading based interval two-stage partial fuzzy programming（LCT-ITPFP）.LCT-ITPFP can solve the dual uncertainties of electricity demand and the ambiguous of predicted power of wind and solar energy while reducing system carbon emissions.It provides an optimal dispatch for industrial park power systems by coupling multi-energy.The comparisons of LCT-ITPFP results against the traditional carbon trading model and model with deterministic probability of electricity demand levels demonstrate that LCT-ITPFP can not only reduce carbon emission from 8.211% to 21.396%,but also avoid the decision failure risk bringing by uncertainties.