Probabilistic production simulation of a power system with wind power penetration based on improved UGF techniques

Abstract

Universal Generating Function (UGF) techniques have been applied to Multi-State System (MSS) reliability analysis, such as long term reserve expansion of power systems with high wind power penetration. However, using simple steady-state distribution models for wind power and large generating units in reliability assessment can yield pessimistic appraisals. To more accurately assess the power system reliability, UGF techniques are extended to dynamic probabilistic simulation analysis on two aspects of modelling improvement. Firstly, a principal component analysis (PCA) combined with a hierarchal clustering algorithm is used to achieve the salient and time-varying patterns of wind power, then a sequential UGF equivalent model of wind power output is established by an apportioning method. Secondly, other than the traditional two-state models, the conventional generator UGF equivalent model is established as a four discrete-state continuous-time Markov model by Lztransform. In the construction process of such a UGF model, the state values are transformed into the integral multiples of one common factor by choosing proper common factors, thus effectively restraining the exponential growth of its state number and alleviating the explosion thereof. The method is suitable for reliability assessment with dynamic probabilistic distributed random variables. In addition, by acquiring the clustering information of wind power, the system reliability indices, such as fuel cost and CO 2 emissions through different seasons and on different workdays, are calculated. Finally, the effectiveness of the method is verified by a modified IEEE-RTS 79 system integrated with several wind farms of historical hourly wind power data of Zhangbei wind farm in North China.

The Economic Viability of Renewable Portfolio Standard Support for Offshore Wind Farm Projects in Korea

“…Most used probabilistic approaches to consider the effects of long-term expansion of variable generation sources on electricity market prices. The most common approach is PPC model, which is favorable to chronological methods in terms of applicability [21,22]. In this paper, we use the PPC-based prediction model [17], which was reformulated to take into account the effects of variable generation sources.…”

A Modified Artificial Bee Colony for Probabilistic Peak Shaving Technique in Generators Operation Planning: Optimal Cost–Benefit Analysis

“…Although there are several methods used for ODLEU based on PPC of the generation units, the probabilistic peak-shaving technique (PPS) within the framework of ELDC with the Frequency and Duration (FD) approach has shown that the method is superior to the other methods used for simulating multiple limited energy units (hydro and pump storage units). In addition, it is shown that the result of PPC can be obtained within the low computation time [35,[43][44][45][46][47][48]. Generally, in this technique, LEUs needs to fulfill two criteria to fully utilize their energies.…”

A novel radial basis function neural network principal component analysis scheme for PMU-based wide-area power system monitoring

“…Inspired by the statistical way of analysing power system data, PCA was successfully introduced into wide-area power system monitoring recently [10][11][12]. In our previous work [13], a distributed real-time learning framework has been proposed to support wide-area monitoring using synchrophasors, aiming to integrate the PCA techniques into an hierarchical incremental learning scheme for decision making.…”