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In demand response programs, load service entity (LSE) can aggregate user as an independent entity to participate in the day ahead energy market, and complete the response target through incentive in the next day. In order to reduce the incentive cost of LSE, a differentiated incentive mechanism that consider the differences in user response flexibility is proposed in this paper. Then, a user response behavior model is established by using the long short-term memory (LSTM) network, with aim of accurately predicting users' response. Subsequently, an optimization strategy combining particle swarm (PSO) and LSTM is proposed, so that the response target can be accurately completed with low cost. Simulation experiments verified that the cost of LSE is close to the theoretical minimum, and can be reduced by 20% compared with the optimal result under unified incentive mechanism. Moreover, it also verified that the proposed strategy has high response accuracy and good stability.

With the gradual consumption of fossil energy, the development and use of renewable energy become more and more important. Distributed photovoltaic power stations have become an important means to efficiently use solar energy for power generation. Under the influence of complex environmental factors, with the continuous expansion of photovoltaic power stations, photovoltaic system accidents occur frequently, and problems in operation and maintenance gradually emerge. In order to improve the informatization level of photovoltaic system and reduce operation and maintenance costs, this paper designs an ensemble model based on cross-validation, which integrates five classical classification algorithms. Through analyzing and processing multidimensional data of photovoltaic system, it realizes the function of fault diagnosis and fault classification of photovoltaic system. By comparing the performance of single model and ensemble model in photovoltaic fault diagnosis, it is found that the ensemble model has better diagnostic performance and can be used for photovoltaic system operation and maintenance and fault diagnosis.

The robustness of power grids plays a very important role in reality. The blackout of power system can make a serious damage for the normal running of whole city or whole area and the loss caused by it is inestimable. This paper aims to propose a simulation method to evaluate the robustness of a power grid and some effective strategies to improve the robustness. Complex network keeps the most important characters of a power grid, and makes the simulation process concise. This paper studies how characters influence the robustness and proposes some conclusions and suggestions for enhancing effectively the robustness by changing these characters. In this paper, for the robustness of a network, it can be found that some parameters like tolerance coefficient are more dominant than others. Some parameters interact obviously, like capacity addition ratio and addition station ratio. Improper capacity addition strategy may have a negative effect on the network. And load node with big degree and small flow is likely an excellent choice to be added capacity by distributed photovoltaic power station. The simulation method and improvement strategy can be used in the engineering of power grid and establishment of distributed photovoltaic power system.