Research On Regional Electricity-Environment System Planning Under Uncertainty

With the development of the economic, electricity industry have rapidly developed in our country, and caused a series of environmental problems. For example, sulfur dioxide, dust, nitrogen oxides, waste water, waste ash and other hazardous material discharged from thermal power plant, is harmful on the environmental and human health. Under this circumstance, electricity-environment system (EES) planning is desired to reduce those problems. In order to meet the rising electric demand, improve energy utilization efficiency, and achieve the goal of energy saving and emission reduction, local authorities and regulatory agencies have to seek comprehensive strategies for EES planning and management, such as optimizing the power supply structure and taking full utilization of the renewable energy. EES planning requests the harmonious development among the electric power generation, energy resources, economic and environment. The decision-makers would analysis the complex of the electricity-environment system, and consider the economic development, energy and environment carrying capacities, and science technologies levels to seek the regular pattern of the economic-energy-environmental harmonious development and optimize the power supply structure. On the basis of analysis the complexity of electricity-environment system and the view of environment economic and system engineering, an interval credibility chance-constrained programming model was developed and applied in EES management. Besides, an inexact electricity-environment system model based on Grey prediction and optimization theory was also provided in uncertainty, a case of Beijing is studied to illustrated the applicability of the proposed models. Through solving the models, comparative analysis are provided between the scenarios with (or without) GHG-emission reduction constraints, respectively. Electric supply schemes were obtained with minimized system cost based on the GHG-emission reduction. The results show that the renewable energy generation technology would develop rapidly and GHG and pollutant emission would reduce in a certain extent when considering GHG-emission reduction constraints, however, the total system cost would increase, this trend is evident over the planning periods. The results are useful for helping decision-makers identify the desired electric power generation patterns, capacity expansion schemes and GHG-emission reduction under complex uncertainties.