Robust Optimization Methods For Energy Systems Management And Planning

Energy plays an important role in society development. As import resources, energy are the basic material for the human survival and social development. The rapid economic development and continual urban expansion lead to the shortage of energy resources and the serious environment problems. Such situations have forced decision makers to contemplate comprehensive and ambitions plans for energy resources management. However, such planning efforts are complicated with a processes that should be considered by decisions makers (e.g., energy production, import, conversion). Moreover, many processes are linked to many factors such as economic, politics and so on. These characteristics results in the complexities, uncertainties and risk of resources systems. It is significant to proposed robust methods for energy resources planning, with considering the complexity and uncertainties as well as many factors (e.g., economic, population and political).This paper aims to develop a serious of integrated inexact optimization methods through coupling interval, fuzzy, stochastic and robust methods, with considering the complexities and uncertainties of energy resources systems. The detailed tasks include:(1) interval stochastic robust optimization method was developed for energy systems planning and carbon dioxide management. These models can effectively assign power demands to different conversion technologies with a minimized system cost under uncertainties and generate optimized electricity generation, capacity-expansion schemes; and can effectively manage CO2-emssion with effective trading scheme;(2) interval programming-power plant site selection model and interval stochastic robust-power plant site selection model were proposed based on the interval-parameter programming and robust optimization, and stochastic programming theores. The results obtained could help generate desired decision alternatives, which would enhance the robustness of biomass power system with a low system-failure risk level. It is particularly useful for risk-aversive decision makers in handling high-variability conditions;(3) an interval stochastic double robust optimization model was developed for planning energy system and managing CO2-emissions. In the modeling formulation, two recourse actions were adopted to make the model robustness, which successfully emphasizing the safety of energy system under high-variability;(4) Two municipal-scale energy systems planning models were proposed for Tianjin's energy systems management, based on the interval-fuzzy method and interval-robust method. The two energy systems planning models could effectively tackle uncertainties that are presented in terms of fuzzy sets, stochastic and discrete intervals through coupling interval-parameter programming, fuzzy flexible programming, robust programming and mixed integer linear programming techniques. The models can provide desired energy resource allocation, electricity generation, heat generation, expansion schemes and emission reduced policy with a minimize system cost and generate decision alternatives to help decision makers identify desired policies.The obtained results indicated that the new developed optimization models make improvement on the traditional optimization methods. Meanwhile, the insufficient for each single traditional inexact programming could be remedied through coupling the other optimization theories. The proposed models are valuable for supporting formulate of local policies regarding energy consumption, economic development, and energy structure, analysis of the effect of CO2trading scheme, and in-depth analysis of tradeoffs between system cost and risk. The modeling results could help generate desired decision alternatives that will be able to enhance resources system robustness with a low system-failure risk level.