A Fuzzy Probability-based Markov Chain Model for Electric Power Demand Forecasting of Beijing, China

In this study, a fuzzy probability-based Markov chain model is developed for forecasting regional long-term electric power demand. The model can deal with the uncertainties in electric power system and reflect the vague and ambiguous during the process of power load forecasting through allowing uncertainties expressed as fuzzy parameters and discrete intervals. The developed model is applied to predict the electric power demand of Beijing from 2011 to 2019. Different satisfaction degrees of fuzzy parameters are considered as different levels of detail of the statistic data. The results indicate that the model can reflect the high uncertainty of long term power demand, which could support the programming and management of power system. The fuzzy probability Markov chain model is helpful for regional electricity power system managers in not only predicting a long term power load under uncertainty but also providing a basis for making multi-scenarios power generation/development plans.

An Interval Probability-based Inexact Two-stage Stochastic Model for Regional Electricity Supply and GHG Mitigation Management under Uncertainty

In this study, an interval probability-based inexact two-stage stochastic (IP-ITSP) model is developed for environmental pollutants control and greenhouse gas (GHG) emissions reduction management in regional energy system under uncertainties. In the IP-ITSP model, methods of interval probability, interval-parameter programming (IPP) and two-stage stochastic programming (TSP) are introduced into an integer programming framework;the developed model can tackle uncertainties described in terms of interval values and interval probability distributions. The developed model is applied to a case of planning GHG -emission mitigation in a regional electricity system, demonstrating that IP-ITSP is applicable to reflecting complexities of multi-uncertainty, and capable of addressing the problem of GHG-emission reduction. 4 scenarios corresponding to different GHG -emission mitigation levels are examined;the results indicates that the model could help decision makers identify desired GHG mitigation policies under various economic costs and environmental requirements.

Study on the Traffic Energy System Model in Urumqi Based on Scenario Analysis Methods

In this study, a traffic energy system model is developed to optimize the traffic system cost of Urumqi, considering energy consumption, pollution emission and travel time. Meanwhile, scenario analysis method is proposed to solve the problem of the extreme weather of traffic, and three scenarios (i.e. 10%, 20% and 30%) of reductions of traffic flow quantity and pollutant emission are examined. The results demonstrate that the medium-type coach will be the promising selection under different scenarios especially in the extreme conditions and the traffic flow reduction scenarios are not the better option for the decision owing to the same cost under the different reduction levels. Moreover, encouraging the medium-type coach traveling and restricting the small vehicle driving would be attractive alternatives for the extreme situation. The proposed model would provide reasonable references for decision makers.

An Interval Programming-based Traffic Planning Model for Urban Vehicle Emissions Management

An interval linear traffic planning model is developed for supporting vehicle emissions limited under uncertainty. The interval linear traffic planning model can address uncertainties of traffic system and vehicle emissions related to system costs and limitation of emission. The interval linear traffic planning model is applicable to complex traffic system. One virtual city as our study object was taken by using the interval linear traffic planning model. In this study, one virtual case and a scenario are provided for three planning periods. The results indicate that the interval linear traffic planning model can effectively reduce the vehicles emission and provide strategies for authorities to deal with problems of transportation system.