Pre-dispatch is an important way for distribution networks to cope with typhoon weather, enhance resilience and reduce economic losses. In order to accurately describe the faults and consequences of components’ failure in the distribution network, this paper establishes a pre-dispatch model to cope with typhoon weather based on line failures consequence analysis. First, Monte Carlo simulation is used to sample the typical fault scenarios of vulnerable lines. According to the location of switchgear, the distribution network is partitioned and a block breaker correlation matrix is established. Combined with the line fault status, a fault consequence model of distribution lines related to the pre-dispatching strategy is established. Then, the objective function is given to minimize the sum of the cost of the pre-dispatch operation and the power outage, and then establish a pre-dispatch model for the distribution network. In order to reduce the computational complexity, PH (Progressive Hedging) algorithm is used to solve the model. Finally, the IEEE-69 test system is used to analyze the effectiveness of the method. The results show that the proposed dispatching model can effectively avoid potential risks, reduce system economic losses and improve the resilience of power grids.
In response to the imbalance between power generation and demand, demand response (DR) projects are vigorously promoted. However, customers' DR behaviors are still difficult to be simulated accurately and objectively. To tackle this challenge, we propose a new DR behavioral learning method based on a generative adversary network to learn customers' DR habits. The proposed method is also extended to maximize the economic revenues of generated DR policies on the premise of obeying customers' DR habits, which is hard to be realized simultaneously by existing model-based methods and traditional learning-based methods. To further consider customers' timevarying DR patterns and trace the changes dynamically, we define customers' DR participation positivity as an indicator of their DR pattern and propose a condition regulation approach improving the natural generative adversary framework to generate DR policies conforming to customers' current DR patterns. The proposed method is applied to hourly electricity price optimization to reduce the fluctuation of system aggregate loads. An online parameter updating method is also utilized to train the proposed behavioral learning model in continuous DR simulations during electricity price optimization. Finally, numerical simulations are conducted to verify the effectiveness and superiority of the proposed method.
Recently, with the large scale of power grids and the increase in frequency of extreme weather, the safe and stable operation of power systems is facing great challenges. Therefore, mobile emergency power source (MEPS) are a promising and feasible way to deal with extreme weather and reduce economic losses. However, the current urban power grid and transportation network are closely coupled, and the congested traffic hinders the rapid configuration of MEPSs. Therefore, this paper proposes an MEPS configuration scheme considering real-time traffic conditions. Firstly, the dynamic road traffic index (DRTI) is defined, which can fully describe the dynamic characteristics of traffic. The wavelet neural network (WNN) is used to predict the traffic flow. Then, combined with the knowledge of graph theory, an A-Star algorithm (AS) is used to determine the optimal path. Secondly, the optimal installation location of MEPSs is determined by forward–backward sweep method in distribution network. Finally, the feasibility, accuracy, and time cost of the proposed method are verified by numerical simulations, which can meet the requirements of online application.
This paper proposes a power system state estimation method combining the Monte Carlo method and the SCADA/PMU hybrid measurement state estimation algorithm. Quantify the impact of load uncertainty on the state estimation results to reduce the influence of power system measurement error. The results show that the method takes into account the uncertainty of load measurement and the influence of flexible load, and accurately estimate the operating state of the power system.
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