Power System Optimal Operation With Large Scale Photovoltaic Generation Under Low Carbon Economy

With the global energy demand increasing, the consumption of coal, oil, and other fossil fuels also increase all the time. The problems of climate change and environmental pollution become more and more serious than ever. Developing “low carbon economy”, improving the energy structure and building a good living environment for human beings has become the consensus of the world. Under this background, the wind power, photovoltaic generation and other renewable energy have developed very quickly, especially to photovoltaic generation, showing an explosive speed in recent years. Compared with the conventional thermal power, the energy source of photovoltaic generation comes from solar energy, which is no pollution and no carbon emissions in the operation. It has an important effect on responding the policy of energy-saving and emissions reduction, which can promote the low carbon development in power industry. But the output of photovoltaic generation has the stochastic nature, if other units with adjustable functions cannot respond quickly, the volatility will destroy the active power balance, which has caused a seriously effect in safety to the power system. Under low carbon economy, the carbon emissions from power system is given the economic value and can be trading in the market. Under this background, how to consider the effect of carbon trading mechanism introduced to power system optimal operation reasonably, and will maximize the operation efficiency of power system, has important research value.The problem of power system optimal operation with large scale photovoltaic generation under low carbon economy is studied in this paper. First, the output characteristics of photovoltaic generation is studied. In view of the present situation of low accuracy in photovoltaic generation output prediction, based on the probability characteristics of photovoltaic generation, the scenario analysis method is used to describe the stochastic nature of photovoltaic generation combined with Latin Hypercube sampling. As K-means clustering algorithm is influenced by the initial clustering centers greatly, this paper introduces an improved K-means clustering algorithm based on Huffman tree to cluster the initial scenarios generated by Latin Hypercube sampling, which can effectively improve the clustering quality. On this basis, this paper puts forward the optimization model of power system unit commitment with photovoltaic generation, which to minimize the expected operating cost in all scenarios. Besides the generation cost, the penalty cost of photovoltaic power abandoned and load loss caused by the stochastic nature of photovoltaic generation is introduced to the model. Through the reasonable unit commitment, on the one hand, the acceptance rate to the photovoltaic generation is improved, on the other hand, the reliability rate to the load is ensured. Based on the related analysis of carbon emissions trading theory, the influence of carbon trading mechanism to power system optimal operation is studied. The baseline emissions factor of power grid is used to distribute the initial carbon emissions credits. On this basis, this paper puts forward the model of power system optimal dispatch with photovoltaic generation under low carbon economy, which to minimize the comprehensive operation cost in all scenarios considering the influence of carbon emissions. It has the advantage of balancing the economy, reliability and low carbon emissions of power system operation, and can better adapt the national policy of greenhouse gas emissions reduction. A ten-unit test system is simulated to verify the proposed model.