Systematic Procedure for Mitigating DFIG-SSR using Phase Imbalance Compensation

Replacing conventional generation by power electronics based generation changes the dynamic characteristics of the power system. This results among others in the increased susceptibility to sub synchronous oscillations (SSO). This paper proposes a systematic procedure for mitigating the interactions between a DFIG and a series compensated transmission line using the phase imbalance compensation (PIC) concept. The impact of the series and parallel PIC on the resonance behaviour of the grid is first thoroughly investigated. Then, the influence of the system strength on the capabilities of the PIC to mitigate DFIG-SSR is assessed. Based on the findings a design framework which enables the systematic assessment of the series and parallel PIC for mitigating DFIG-SSR is developed and successfully implemented in the IEEE 39 bus system. Comparison between both concepts reveals that the parallel PIC is better suited to mitigate DFIG-SSR. The impedance based stability analysis and detailed time domain electromagnetic transient (EMT) simulations are used to screen and validate the results.Intelligent Electrical Power Grid

Evaluation of PV and QV based Voltage Stability Analyses in Converter Dominated Power Systems

PV and QV analyses have been widely used in industry. It has already been proven that these steady state methods can be used to assess power system's load ability from voltage stability perspective and that their use in terms of accuracy is justified when compared to time domain simulations. However, this prior validation was carried out for conventional synchronous generator dominated power systems. With increasing levels of power electronics interfaced generation (PEIG) being integrated in power systems, the accuracy of the PV and QV methods for these `green' power systems can be challenged. This paper investigates to what extend the use of these methods is justified when the power system faces a displacement of conventional generation with PEIG. To this end, assessments with the IEEE 9 bus system and full converter wind turbine generators have been performed in this study. It is shown that, when compared to time domain simulations, the traditional PV and QV analyses do not always accurately predict the saddle-node bifurcation point. Steady state PV analyses show inaccuracies between 1.8% and 16.8% (when compared to time domain simulations) in identification of the instability point. The mismatch between steady state and time domain QV analyses is between 6.1% and 22.9%. Based on the achieved results, QV analysis is shown to be typically less accurate than PV analysis for PEIG rich systems

Long-Term Electricity Load Forecasting Considering Volatility Using Multiplicative Error Model

Long-term electricity load forecasting plays a vital role for utilities and planners in terms of grid development and expansion planning. An overestimate of long-term electricity load will result in substantial wasted investment on the construction of excess power facilities, while an underestimate of the future load will result in insufficient generation and inadequate demand. As a first of its kind, this research proposes the use of a multiplicative error model (MEM) in forecasting electricity load for the long-term horizon. MEM originates from the structure of autoregressive conditional heteroscedasticity (ARCH) model where conditional variance is dynamically parameterized and it multiplicatively interacts with an innovation term of time-series. Historical load data, as accessed from a United States (U.S.) regional transmission operator, and recession data, accessed from the National Bureau of Economic Research, are used in this study. The superiority of considering volatility is proven by out-of-sample forecast results as well as directional accuracy during the great economic recession of 2008. Historical volatility is used to account for implied volatility. To incorporate future volatility, backtesting of MEM is performed. Two performance indicators used to assess the proposed model are: (i) loss functions in terms of mean absolute percentage error and mean squared error (for both in-sample model fit and out-of-sample forecasts) and (ii) directional accuracy

Slow Coherency Identification and Power System Dynamic Model Reduction by using Orthogonal Structure of Electromechanical Eigenvectors

Identifying generator coherency with respect to slow oscillatory modes has numerous power system use cases including dynamic model reduction, dynamic security analysis, or system integrity protection schemes (e.g., power system islanding). Despite their popularity in both research and industry, classic eigenvector-based slow coherency techniques may not always return accurate results. The multiple past endeavors to improve their accuracy often lack a solid mathematical foundation. Motivated by these deficiencies, we propose an alternative consistent approach to generator slow coherency. Firstly, a new approach is introduced to accurately detect slow coherent generators by effectively minimizing generic normalized cuts. As a by-product, the new approach can also guide the choice of the number of slow coherent groups. Secondly, it is shown that the combination of the the proposed slow coherency approach and an enhanced version of the inertial generator aggregation method allows to produce accurate dynamic equivalents even if the selected number of generator groups is relatively low

The impact of energy storage on long term transmission planning in the North Sea region

This paper presents a planning framework to investigate the impact of different levels of integration of large-scale energy storage on the development plan of a meshed HVDC grid in a power system with large-scale offshore wind. In our problem formulation, the charge/discharge schedules of energy storage are modeled in such a way that market conditions in the succeeding hours are taken into account in the power dispatch at present. Both unlimited and limited energy storage capacity scenarios are considered, and compared to a no-storage reference case. The optimal plan includes grid topology, transmission capacities, energy storage capacities and optimal energy storage schedules. The optimization model sets the transmission capacities in such a way that transmission congestion revenue collected throughout the lifetime of the infrastructure pays off the investment cost of building the grid. The proposed model is applied to study the future development of an offshore grid in the North Sea. Simulation results are assessed according to various economic indicators. Investing in energy storage is shown to be economically effective for windy offshore regions

A multivariate framework to study spatio-temporal dependency of electricity load and wind power

With massive wind power integration, the spatial distribution of electricity load centers and wind power plants make it plausible to study the inter-spatial dependence and temporal correlation for the effective working of the power system. In this paper, a novel multivariate framework is developed to study the spatio-temporal dependency using vine copula. Hourly resolution of load and wind power data obtained from a US regional transmission operator spanning 3 years and spatially distributed in 19 load and two wind power zones are considered in this study. Data collection, in terms of dimension, tends to increase in future, and to tackle this high-dimensional data, a reproducible sampling algorithm using vine copula is developed. The sampling algorithm employs k-means clustering along with singular value decomposition technique to ease the computational burden. Selection of appropriate clustering technique and copula family is realized by the goodness of clustering and goodness of fit tests. The paper concludes with a discussion on the importance of spatio-temporal modeling of load and wind power and the advantage of the proposed multivariate sampling algorithm using vine copula