Research On Reduction Potential Analysis And Structure Optimization Model For Electric Power Industry In Shandong Province

With the development of industrialization and new urbanization, and the growing demand for energy, especially electricity resources, the problems of energy demand-supply and energy environmental security have been becoming more and more serious in Shandong province. Therefore, from the energy-environment system development faced with the problem, many kinds of complexities and uncertainties existing in energy and environment system would be analyzed deeply. And the energy planning and research work in the power industry would be carried out, combined with the national energy and environment systems development and planning policies and measures, research on energy system planning.Then take power industry’s "12th five-year" emission-reduction as an example, the province scale of CO2, SO2, NOx and PM emissions accounting was analyzed, combined with the enacted energy-saving emission reduction policies and measures. The analysis results showed that Shandong province’s electric power system still faces serious pressure on energy conservation and emission-reduction during, and it can effectively relieve urban energy environmental bearing capacity in the period of twelfth five-year plan, though changing the energy structure and developing clean energy and new energy. And Shandong province should pay more attention to the development of management emissions based on project emissions and structure of emission reduction, and actively carry out carbon emissions trading mechanisms. Based on the analysis of electric power industry’s energy saving and emission reduction, a province scale of electric-environment system planning model is developed and combined the interaction mechanism between the supply and demand of energy, environmental protection, and economic development.The results show that, the developed model based on a multistage interval-stochastic integer linear programming and conditional value-at-risk (CVaR) measure, could effectively deal with uncertainties expressed as probability distributions and interval values existing, and reflect the risk-aversion in energy system planning problems. The obtained solutions could provide useful decision alternatives under different pollutants emission reduction policies and various risk aversion scenarios. The results are valuable for supporting the adjustment or justification of the air pollution mitigation management and electric power planning schemes within a complicated energy system under uncertainty.