The energy-saving dispatch could significantly enhance the energy consumption and carbon dioxide emission reduction, as well as the sustainable development of the socio economy in China. With the rapid growth of smart grid, the use of demand response to dispatch loads with flexible consumption time and/or quantity has been a new trend in power industry. This paper proposes a new energy-saving dispatch problem while considering energy-saving and emission-reduction potentials of generation and demand sides, as well as the interaction between the two. A bi-level optimization model is established to address the interaction between the energy-saving dispatch of thermal units and that of users. The objective function of the upper level considers both the electric power generation cost and the carbon emission cost of thermal units, while the lower level integrates both compensation and incentive costs of electricity consumers into its objective function according to the influences of their regulation-based demand response to the power grid. Moreover, user benefits of reducing downtime and avoiding frequent load restarting are also considered in the lower layer model. An iterative algorithm is proposed and the improved nondominated sorting genetic algorithm II (NSGA-II) method is used to solve the lower-layer model for seeking the optimal compromise solution on the Pareto frontier, which is derived by maximum deviations and entropy-based multiple attributes decision making method. Comparing with general NSGA-II and multiobjective genetic algorithms, the improved NSGA-II method can improve the spatial distribution of Pareto solution set and reduce the number of iterations, thus having stronger consistency among multiple objective functions and better performance.
All Science Journal Classification (ASJC) codes
- Computer Science(all)