Multi‐period probabilistic‐scenario risk assessment of power system in wind power uncertain environment

Quantitative Entropy Weight TOPSIS Evaluation of Sustainable Chinese Wind Power Developments

“…Based on an adaptive fuzzy neural network, Pinson and Kariniotakis constructed an advanced wind forecast system to predict the online risks of wind farms due to inaccurate weather forecasts [7]. Deng et al examined multiperiod probability power system risk evaluations in uncertain wind power environments and built a tool to evaluate the power grid safety risks of short period wind power [8]. Based on triangular fuzzy numbers, Li and Sun proposed a fuzzy analytic network process to build a risk evaluation index system for assessing the risks of maritime wind power projects and then demonstrated its viability on a run-time risk evaluation during the construction of a maritime wind farm in Hangzhou Bay [9].…”

An Integrated LHS–CD Approach for Power System Security Risk Assessment with Consideration of Source–Network and Load Uncertainties

“…In order to conduct the risk analysis, the uncertain input variables shall be sampled properly in terms of a sampling matrix, which should reflect the completed power system source-network-load uncertainties. Based on the probabilistic model of transmission network outages, wind power, and thermal power output, as well as the load demands presented in Section 2, the sampling matrices of thermal generators and transmission lines are generated as independent input variables according to the procedure in Section 3.2.1, and the sampling matrices of correlated wind speeds (which was later converted into wind power samples based on the wind speed-power curve according to Formulas (5) and (6)) and correlated load demands are generated according to the steps in Section 3.2.2. The final input variable sampling matrix X F is generally formed as:…”

“…In recent decades, the application of risk assessment technology for wind power penetrated power systems has attracted high academic interest [5]. In [6], an uncertain model for short-term wind power generation was established, and the conditional risk value-based safe distance (S-D) was intended to reveal the tail risk of system operation. On the basis of S-D, four new risk indicators were defined to reflect the risks of short-term wind power output changes in the near future.…”

Research and application of efficient risk analysis method for electric power grid multiple faults under widespread wildfire disasters

“…These types of methods are suitable for small local urban power grid, but are inefficient for multiple failure risk analysis of a large power grid. The second type of methods are based on random sampling. Zhao proposed a fast sampling method based on Monte Carlo sampling for grid risk assessment and system implementation; Wang reported a study of risk assessment using Monte Carlo simulation for the safe operation of a large power grid; Chen conducted intensive studies of risk assessment of power grid operation modes based on Monte Carlo simulation; Zhu carried out a reliability evaluation of Sichuan Power Network using Monte Carlo simulation technique; Shi et al proposed a power grid risk analysis method based on assuming the fault obeys normal distribution; He et al evaluated the risk in urban network planning based on fuzzy theory, and the probability of the risk factors are assessed by questionnaire; Cheng et al put forward a power system reliability evaluation method based on the assumption that component's fault is normal distribution; Li et al raised a transmission line overload risk assessment method, and the transmission line overload fault is assumed to be normal distribution; Deng et al proposed a risk assessment of power system in wind power uncertain environment, and the wind power output is assumed to be normal distribution; Wang et al and He et al assumed that probability distribution of transmission lines or transformer faults is approximately Weibull distribution; and it is very common that the transmission lines tripping probability or failure rate of a given unit in power system is assumed to be a fixed value in several studies . These methods have good computational efficiency for risk assessment of a large power grid.…”