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Abstract. To ensure the security and stability operation and reliable power supply, it needs to take appropriate grid dispatching approaches to management, combining with the actual operation of the power grid parameters, such as voltage, current, frequency, load, considering the case of the production work. Generation dispatching model is important for the safety of grid security and stability. Scale renewable energy generation is an important way to promote green economic development and energy-saving emission reduction targets. However, wind power and photovoltaic power generation started late, and the technology is immature, scale renewable energy and power generation will have an impact on the traditional generation dispatching model. This paper classifies and compares the existing renewable energy generation dispatching model, and evaluates the renewable energy generation dispatching model of the UK and Northern Europe. In the base of Combining with China's implementation of energy-saving dispatching model, this paper got enlightenment in renewable energy generation dispatching model of China.

The medium and long term load forecasting is the basis of power planning, investment, production, scheduling and trade, which

Abstract. Since load forecasting plays an important role in the planning and operation of power industry, substantial efforts are made in improving the accuracy and reliability of load forecasting. In this paper, we develop a novel hybrid approach based on phase space reconstruction and least square support vector for the short-term load forecasting. However, the proper parameters in phase space reconstruction and least square vector machine have a significant effect on the forecasting performance, and there is no standard solution for the parameter estimation problem. Therefore, in this paper, the genetic algorithm (GA) approach is employed to optimize the parameters of both phase space reconstruction and least square support vector machine together. The experimental results suggest that the joint optimization parameter is superior to the separate optimization solutions.

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