Liu Yunjia

Independent microgrid day-ahead optimization based on demand response

According to absorb renewable energy and smooth the load curve of independent microgrid, a strategy of demand response optimization, including controllable distributed generation, energy storing device and shiftable loads resource, is proposed. According to the day-ahead power forecast of renewable energy generation and electric power users load, the strategy draws the payload power curve. The objective function is to minimize the difference between power supply and demand. Then, a day-ahead optimization model based on demand response is established. The results of the model are used to obtain the working condition at a certain time in the future, such as the switching state of the controllable distributed generation, the working state of the shiftable loads, the reference charging power and discharge power of the energy storage device, and reference state of charge. The example shows that the proposed optimization strategy can effectively reduce the load during the peak period of power consumption, reduce the residual power of renewable energy generation and increase the load rate of the controllable distributed generation. Meanwhile, this method predicts renewable energy generation and the short-term power of the load. According to the level of prediction error, the running state is coordinated and controllable among the controllable distributed generation, shiftable loads and energy storage device, providing an adjustable power margin for real-time scheduling of individual microgrids.

Fault diagnosis method of distributed power distribution network based on advanced Petri net

A DG-based distributed fault diagnosis method based on BP neural network with dynamic adaptive fuzzy Petri nets is proposed to solve the problem that traditional fault diagnosis methods lead to complex matrix and switching functions. In this paper, the general fault diagnosis model is constructed, and the simplified model of protection information is processed in the form of sets. If the operation mode and protection are changed, the model need not be reestablished, and the logic of the protection circuit breaker error correction is used with high fault tolerance. Secondly, BP algorithm is used to train the fuzzy parameters in the model. Finally, simulation test is carried out for the distribution network with DG, which verifies the reliability and fastness of the method.