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Sensor errors, model uncertainty, changes in the surrounding environment, data loss, or malicious network attacks can all result in outliers, which can pollute the measurement process of the distribution system. In this study, to address this problem, a dynamic state estimation method for distribution systems based on innovation-saturated Koopman Kalman filter (IS-KKF) was proposed. The Koopman Kalman filter (KKF) is model-free and data-driven, and estimates nonlinear dynamics through the conventional linear Kalman filter. To make the Koopman Kalman filter robust to measurement outliers, the innovation saturation mechanism applies a saturation function to it. To correct the state estimation, this mechanism is applied to the filtering process. When outliers occur, the distorted innovation is saturated to prevent the state estimation results from being destroyed. Adaptive adjustment of the saturated boundary is a feature of this mechanism. Extensive simulations were carried out on the IEEE 118-bus test system to verify the effectiveness and robustness of the proposed method. The simulation results demonstrate that the proposed method can effectively reject outliers with different amplitudes, types, and continuous times, has significant computational efficiency, and does not require additional measurement redundancy.

Dynamic state estimation (DSE) plays an important role in the real-time control and monitoring of distribution systems, which are high-dimensional space–time systems. The degree of nonlinearity of distribution system increases drastically with the availability of sustainable energy and the diversification of load types, thereby reducing the accuracy of the standard linear model. Under the existing schemes, the measurement noise may not follow a Gaussian distribution. Therefore, a Koopman operator-based Kalman particle filter (KKPF) is proposed herein for estimating the dynamic states of a distribution system. The KKPF performs data-driven dynamic state estimation based on the Koopman operator theory, which does not rely on the distribution system model and can be applied to high-dimensional systems. Furthermore, this method can be applied to dynamic systems and noise with both Gaussian and non-Gaussian distributions. The KKPF method provides accurate estimation results when only a small number of measurements are available. IEEE 141 system and an improved 141 system were used to test the performance of the proposed KKPF compared to the EKF, CKF, and PF. The test revealed that the proposed KKPF can obtain accurate results under high-dimensional and non-Gaussian noise environments.

In light of the challenges posed by the widespread distribution of new energy sources in China and their distance from load centers, the power system must effectively integrate both new energy and thermal power transmission. To address this issue, we propose a dynamic coordinated scheduling model that combines wind, photovoltaic, and thermal power to optimize the profit of the energy complementary delivery system. Additionally, we present an improved ant lion optimization algorithm to investigate the coordinated scheduling and benefit distribution of these three power sources. This paper introduces a cooperative mode for benefit distribution and utilizes an enhanced Shapley value method to allocate the benefits of joint operation among the three parties. The distribution of benefits is based on the contribution of each party to the joint proceeds, considering the profit levels of joint outbound and independent outbound modes. Through our analysis, we demonstrate that the upgraded ant lion optimization algorithm facilitates finding the global optimal solution more effectively within the feasible zone. Furthermore, our suggested three-party combined scheduling model and profit-sharing approach are shown to be superior and feasible.