Research On Power Supply-Demand Early Warning And Load Optimization In Beijing Area

The healthy development of power industry is very important to make people’s livelihood standards better and assure the rapid growth of the national economy. There have been several large-scale and periodic power supply crisises occurred since the founding of the People’s Republic of China, which seriously restrict social and economic development. In recent years,with the expansion of installation scale electricity unbalance across the country has been solved except the risk of regional short-term electricity supply-demand unbalance at particular period. So it is very necessary to develop power supply-demand warning. Load optimization measures should be taken immediately when getting the electricity unbalance alarm. Traditional load optimization is formulated only based on experience a single target, without considering social impact. Therefore, it is extremely necessary to establish a new multi-objective optimization model taking account into many-sided interests and requirements to provide a more appropriate and effective implementation.Firstly, the basic theories of Power supply-demand warning and load optimization are analyzed in detail. And common electricity demand forecasting algorithms are summarized and analyzed in this chapter. Load optimization measures are demand response and direct load control. Then discusses the general implementation mechanism of demand response and comparatively analyses the direct load control optimization model and the optimization algorithm.Secondly, load optimization model is built according to the different requirements of all parties involved in load optimization project. The objective function is built based on the minimum maximum load of power system, the maximum economic benefits of users, the highest customer satisfaction and the project equity. The constraint conditions are continuous interruption time, continuous running time, maximum times of interrupts. The optimization algorithm selects NSGA2, which principle and calculation process are in-depth study.Finally, this paper takes one day in the summer of2013in Beijing as an example to forecast the power demand at peak period with Elman neural network, which is compared with the power supply capacity to obtain the early warning results. The results show that there is a risk of insufficient electricity supply at peak period of the day. So load optimization model is built and load optimization scheme is formulated according to the actual situation of Beijing. Five typical obtained schemes of Pareto front-end can be chosen, which are based on the actual situation and the emphasis point of implementer. The most comprise scheme can be obtained with optimal solution set based on Pareto font-end. The schemes show that using the multi objective optimization load model established in this paper can formulate several optimized implementation schemes which are focusing on every target or taking account into many-sided interests and requirements.