The growing penetration of photovoltaic (PV) systems may cause an over-voltage problem in power distribution systems. Meanwhile, charging of massive electric vehicles may cause an under-voltage problem. The over- and under-voltage problems make the voltage regulation become more challenging in future power distribution systems. Due to the development of smart grid and demand response, flexible resources such as PV inverters and controllable loads can be utilized for voltage regulation in distribution systems. However, the voltage regulation needs to calculate the nonlinear power flow; as a result, utilizing flexible resources for voltage regulation is a nonlinear scheduling problem requiring heavy computational resources. This study proposes an intelligent search algorithm called voltage ranking search algorithm (VRSA) to solve the optimization of flexible resource scheduling for voltage regulation. The VRSA is built based on the features of radial power distribution systems. A numerical simulation test is carried out on typical power distribution systems. The VRSA is compared with the genetic algorithm and voltage sensitivity method. The results show that the VRSA has the best optimization effect among the three algorithms. By utilizing flexible resources through demand response, the tap operation times of on-load tap changers can be reduced.
With the development of the economy, electricity demand continues to increase, and the time for electricity consumption is concentrated, which leads to increasing pressure on the voltage regulation of the distribution network. For example, a large number of electric vehicles charging during a low-price period may cause the problem of under-voltage of the distribution network. On the other hand, the penetration of distributed power generation of renewable energy may cause over-voltage problems in the distribution network. This study proposes a Stackelberg game model between the distribution system operator and the load aggregator. In the Stackelberg game model, the distribution system operator affects the users’ electricity consumption time by issuing subsidies to decrease the frequency of voltage violations. As the representative of users, the load aggregator helps the users schedule the demand during the subsidized period to maximize profits. Case studies are carried out on the IEEE 33-bus power distribution system. The results show that the time of the subsidy can be optimized based on the Stackelberg game model. Both the distribution system operator and the load aggregator can obtain the optimal economic profits and then comprehensively improve the operating reliability and economy of the power distribution system.
A sensorless control system of a permanent magnet synchronous motor based on an extended Kalman filter (EKF) algorithm faces problems with inaccurate or mismatched process noise statistics. This problem affects the performance of the filter, resulting in an inaccurate estimation of motor speed. To address the above problem, this paper proposes a parameter-adaptive Kalman filter algorithm that does not depend on precise noise system covariance. This method can significantly reduce the negative impact of the noise statistical mismatch on motor speed estimation. In addition, the method uses adaptive covariance prediction and removes the original covariance checks in the EKF, thus reducing the calculation burden. The simulation results show that, compared with the traditional EKF algorithm, the algorithm proposed in this article can effectively reduce the steady-state jitter and improve the filtering adaptability and calculation accuracy.
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