Carbon emission reduction is an urgent global task. Renewable energy sources integration can promote the transformation of cleaner and greener power system. But the time-varying nature of these sources causes indeterminacy problems. Smart grid is a powerful tool that can deal with these problems in electricity aspect. One of the key smart grid technologies is demand side management. How to use demand side management to regulate and decarbonise the power system is the main point of this thesis.
In order to integrate renewable energy sources, a day-ahead electricity market scheme is proposed, involving the utility, the demand response aggregator and customers. This model leads to a multiobjective optimization problem, which is solved by an artificial immune algorithm. The simulation results confirm the feasibility and robustness of the proposed model. All participants can benefit from it, and the system power peak to average ratio can be reduced.
In order to realize the carbon emission reduction, a system model for annual fuel sources scheduling and operational policy making of electricity generation is established, considering the economic, environmental and social aspects. A minimum Manhattan distance approach is proposed to select the final solution. The impacts of carbon tax and renewable obligation on carbon emission, generation cost and electricity bill are examined. These can reveal the proper strategy for deciding renewable energy source and carbon emission related policies.
After that, a carbon emission flow model is introduced to facilitate the analysis and assessment of demand side management’s impacts on carbon emission reduction. The time sensitivity of carbon emission in both generation side and customer side are obtained. The daily case and seasonal case are presented. The simulation results show that the load curtailment and load shift approaches can effectively reduce the carbon emission
