Research On Low-carbon Development Models And Decision Support System Design For China’s Electric Power Industry

Electricity is not only the essnetial driving forece factor for natioan enocmic grwoth but also the most important secondary energy form for terminal consumption. China’s power generation sector relies heavily on coal and its products, which makes it the largest CO2 emitter of all the industrial sectors. The power industry plays a vital role in reducing China’s CO2 emissions due to its huge potential of emission reduction. The development of low-carbon electricity industry is a necessary sustainable route to coordinate economy, society and environment. Therefore, research on national lower-carbon oriented development of electricity industry is of importance in controlling CO2 emissions in electricity industry, echoing climate change and realizing the goal of energy-saving and carbon-reduction. Based on the low-carbon economy theory, low-carbon oriented electricity industry development mode and route are studied systematically by the means of multi-cross view of national, industry, province and generation, terminal use to achieve the goal of CO2 emission controlling in power industry. The main research contents are listed as follows.(1) The future trend of CO2 emissions from electricity industry has been predicted scientificly from the quantitative aspect. The QHAS-LSSVM-based prediction model for environmental pressure in electricity industry is provided. The presented QHAS-LSSVM is applied to generation coal consumption and the corresponding carbon emissions forecasting. The results can show the environmental pressure of power industry from a quantitative perspective so as to stir the carbon-reduction responsibility.(2) Based on the traditional STIRPAT model, the technological factor is extended according to the characteristic of electricity industry. In order to solve the extreme multicollinearity among the variables, PLS is adopted to estimate the parameters of the linear STIRPAT model. Through calculating the VIP value of the variables, important ones can be identified which have manifest impact on carbon emissions from power industry. And these recognized factors are also used as the input variable in system dynamic model for power industry to design the possible low-carbon development modes.(3) With the combination of driving forces and policy, possible future development paths for low-carbon electric industry are designed and simuated. A system dynamics model based on dynamic interactions among system influential components is constructed to analyze the CO2 emissions from power electric power industry under three designed scenarios considering the low carbon electricity policy, carbon tax, electricity price and powe technology. Through scenarios design, the total CO2 emissions, CO2 emission coefficient of electricity production and CO2 emission coefficient of electricity consumption are calculated, which can have certain instructive hints for low-carbon oriented electricity development.(4) From the aspect of provincial level, the similarities and differences between different regional electric CO2 emission have been analyzed systmatically in order to fomulat the local development policy for low-carbon electricity industry. The Affinity Propagation (AP) algorithm is applied to find the similar characteristics in emissions among 30 provinces. The regional differences in driving forces on CO2 emissions from power industry are examined using refined Laspeyres decomposition model. Results showed that there are significant contribution differences of five indicators (power generation emission coefficient, generation structure, electricity intensity, economy and population) on power generation emissions among different provinces. The provincial emissions reduction target and supporting policies for power industry should be customized and consistent with the actual situations considering the similarity and differences in emission characteristics.(5) Considering the coordination of economic development and enviromental resource protection from the terminal point, the electricity carbon productivity (ECP) is decomposed from the view of industry. Similar to carbon productivity, electricity carbon productivity (ECP) is defined first. Then multi-dimensional decomposition method is applied to ECP time series decomposition in order to explore the contribution of technological improvement and structure adjustment for each industrial sector from the final electricity aspect. According to the decomposition results, a roadmap for raising carbon productivity by reducing emissions with a minimal impact on electricity demand is provided, such as:electricity consumption structure optimization for industry, electricity consumption efficiency improvement, cultivating energy-saving services industry and structural energy-saving.(6) A scientific low-carbon electricity development evaluation system is established to appraise the development effect in China’s electricity industry. First, the evaluation indicators system of electricity low-carbon development is constructed The Bayesian Truth Serum-Anti Entropy (BTS-AE) approach combining improved osculation value method is proposed. BTS-AE is a novel technique to determine the indicators’weights which combines the subjective weighs and objective weights effectively. Then, the improved closing degree based osculation value method uses the combination weights as its own input weights to complete the whole calculation process. And according to calculated closing degree based osculation (CDO) value, the low-carbon development level can be evaluated, which can provide the development direction suggestion for future development.(7) The Power Industry Low-carbon Development Decision Support System (PILCDDSS) is designed, which integrates the generation coal consumption and the corresponding carbon emissions prediction model, carbon emissions driving factors identification and scenarios simulation model for electricity industry, different low-carbon oriented development routes for regional electricity industry, electricity carbon productivity decomposition model and overall low-carbon electricity development evaluation model. Through the means of computer technology and simulation technology, PILCDDSS can provide references and scientific suggestions for decision making in low-carbon electricity development by man-machine interaction mechanism.