Samer Sulaeman

A Wind Farm Reliability Model Considering Both Wind Variability and Turbine Forced Outages

This paper presents an analytical method to model the output of large wind farms for power system reliability assessment. The model considers the variability of wind, random failures (forced outages) of turbines, as well as the correlation between turbine outputs that results from the turbines on a farm being subjected to similar incident wind speeds. Due to this correlation, the outputs of individual turbines cannot be modeled as independent random variables. The problem is addressed here by separately modeling the independent outages of wind turbines and the dependency on wind speed, and then convolving the two distributions. The resulting model includes both probability and frequency distributions of the power output of the wind farm. The proposed method is demonstrated on the IEEE Reliability Test System. Monte Carlo simulation is used to validate the results.

Transient stability of distributed generators in the presence of energy storage devices

Several small-scale distributed generators DGs have been recently used in power and distribution systems. Placement of such small-generators to the power grid has numerous economical and environmental advantages. However, high penetration level of these devices could also have a significant impact on the voltage and angle stability limits of the system. This can be partly attributed to the fact that most DGs are inertia-less generators or sometimes having small inertia. Therefore, in non-conventional power systems where DGs are present, the overall system inertia will significantly decrease. Such low inertia may lead to loss of synchronism during abnormal fault conditions and large disturbances. One method to compensate for the reduced system inertia is to connect short-term energy storage to the DG bus. The entire unit is termed as virtual inertia or Virtual Synchronous Generator VSG. This paper introduces a methodology for stability enhancement of non-conventional power systems. The method utilizes the concept of virtual inertia to account for the transients that usually take place on power system. A control algorithm was integrated to the storage device that is connected to the DG bus through bidirectional DC-DC converter and DC-AC converter. The method was demonstrated on a 5-bus power system.

Quantification of Storage Necessary to Firm Up Wind Generation

This paper proposes a method to quantitatively determine the sizes of energy storage systems that are intended to mitigate negative impacts of integrating wind energy into power systems. Although the integration of wind power has several advantages, it poses several technical challenges such as variability and uncertainty of wind speed and failures of wind turbine generators (WTGs), which may deteriorate the reliability of power systems. One of the most practical solutions to mitigate these drawbacks is the use of energy storage systems. The method proposed in this paper determines the sizes of the energy storage systems considering the effect of wind power uncertainty and variability, failures of WTGs, wind speed temporal resolution, and correlation with system load. Sizes of energy storage systems are determined based on composite system reliability analysis under operational and technical constraints using the ac power flow model. Monte Carlo simulation is used to emulate the behavior of the system. The proposed method is demonstrated on the IEEE reliability test system and the results are provided. The results show that the size of an energy storage system is dependent on wind farm characteristics, as well as the connectivity with the rest of the system.

Evaluation of wind capacity credit using discrete convolution considering the mechanical failure of wind turbines

In view of the increasing role of wind power generation, there is an evolving body of reliability methods that are concerned with improved modeling of wind generation and related phenomena. An important consideration in the planning of wind generation projects is the capacity value of the farm at the proposed location. The modeling considerations in this process should take into account not only the variable nature of wind and the mechanical failure of turbines, but also the correlation between the individual turbines on the farm. This paper introduces an analytical method to calculate the capacity credit of wind farms including the mechanical failure of wind turbines. The proposed method is based on the discrete convolution technique and takes into account the stochastic nature of wind power as well as the forced outage rates (FOR) of wind turbines. The discrete convolution method has been used in this work to build a generation model in the form of a capacity outage probability table (COPT). A comparison of wind power capacity credit with and without considering the mechanical failures of wind turbines is shown to demonstrate the impact of turbine failure. Also, the capacity credit is calculated based on two reliability indices which are Loss of Load Expectation, LOLE, and Loss of Energy Expectation, LOEE. The proposed method is applied on the IEEE RTS-79 and the hourly wind speed data were taken from Abee Agdm Alberta, Canada. The results show the importance of inclusion of FOR of wind turbines on estimating wind power capacity credit. The results are validated using Monte Carlo simulation.

Modeling the output power of PV farms for power system adequacy assessment

This paper presents a method to model the expected output power of PV systems and to evaluate their effect on power system reliability. Grid level PV systems are usually constructed from a large number of electronic components and converters. Modeling of these systems in power system reliability is a complex task due to the dependency of the output power on an intermittent source (solar) and the availability of a large number of system components. An analytical method to construct a capacity outage probability table (COPT) that captures both the intermittency of the input source and component failures was proposed to model PV systems in power system adequacy assessment. The intermittency of the input source and component availabilities were modeled separately and then convolved to construct a single COPT. The resulted COPT can be viewed as a single PV system with multi-derated states. System reliability indices were evaluated using discrete convolution method. Monte Carlo simulation was used to validate the results of the proposed method. The proposed method was implemented on the IEEE-RTS and Roy Billinton test system (RBTS) with a wind farm of 30 MW. The obtained results were very close to those obtained using Monte Carlo simulation with less computation effort.

A method to model the output power of wind farms in composite system reliability assessment

This paper introduces a method to estimate the output power of a wind farm for composite reliability evaluation. The proposed method is presented in terms of capacity outage probability and frequency table (COPAFT) considering the mechanical failure of wind turbine generators (WTG) and the correlation between the outputs of turbines on a wind farm. Based on the proposed method, the output power of a wind farm is modeled as a single source with multiple derated states. The method has been applied on the IEEE RTS to investigate the effect of considering the mechanical failures of wind turbines on system reliability indices. The wind power is allocated at different buses of the IEEE RTS to calculate the well known reliability indices. The obtained results confirmed that locating wind farms at the buses that at a high risk enhances the overall reliability of the system than placing these farms at the buses of low risk. The proposed method provides a straightforward steps to estimate the outage of wind power due to input availability and mechanical failure of WTG. Further, it reduces the complexity of modeling the output power of wind farms considering the stochastic nature of wind speed and WTG mechanical availability.

Evaluation of wind power capacity value including effects of transmission system

In recent years, investigations of the effects of the intermittency and uncertainty of wind power on power system reliability have gathered significant momentum. This paper proposes a method to model large wind farms and determine their capacity values in a manner that considers both the mechanical failures of wind turbines and the effects of the transmission system. The proposed method combines the probabilistic model of wind power and mechanical failures of wind turbines by sorting wind speeds into bands. While most methods estimate the capacity value based on generation adequacy, the method presented here also captures the effects of transmission constraints by using a composite system reliability analysis approach. Wind power capacity values were evaluated and compared for different scenarios taking into account the effects of operation and transmission line constraints and mechanical failures of wind turbines. Capacity values of wind power in composite system were compared with those of using adequacy assessment. The proposed method was applied on the IEEE-RTS to calculate the reliability indices and capacity values of wind power. The obtained results demonstrated that system operation and transmission line constrains have significant impacts on capacity values of wind farms. Also, the results showed that ignoring mechanical failures of wind turbines produces conservative assessment of the reliability indices and capacity values.

Modeling and assessment of PV solar plants for composite system reliability considering radiation variability and component availability

This paper presents a method to model the output power of large PV systems for composite system reliability assessment. Grid level PV systems are usually constructed from a large number of power electronic components and PV panels. Modeling of these systems in power system reliability is a complex task due to the dependency of the output power on the intermittent source (solar radiation) and the availability of a large number of system components. An analytical method to construct a capacity outage probability and frequency table (COPAFT) that captures both the intermittency of the input source and component failures is proposed to model PV systems. The intermittency of the input source and component availabilities are modeled separately and then convolved to construct a single COPAFT. The resulting model includes both probability and frequency distributions. The proposed method reduces the complexity of modeling and evaluating large PV systems for composite system reliability assessment. The method is demonstrated on IEEE Reliability Test System (IEEE RTS). Considering the PV farm location with a view to enhance system reliability, sensitivity study is conducted to measure the effect of the location of the PV farm on overall system reliability. The results confirm that connecting PV farms to the buses that are at a high risk enhances the overall system reliability.

Optimal location and size of distributed energy resources using sensitivity analysis-based approaches

This paper introduces an analytical approach based on sensitivity analyses of various objective functions with respect to load constraints to determine optimum locations and sizes of distributed energy resources (DERs). This method is based on sequentially calculating Lagrange multipliers of the dual solution of an optimization problem for various load buses. Determining the best candidate locations based on the sensitivity analyses with the assumption that an active constraint would remain active for all source sizes could produce inaccurate results. The reason is that buses that are ranked as the best candidates based on Lagrange multipliers may not be valid for large DERs since Lagrange multipliers change with the change in the system loading. In this work, locations and sizes are jointly determined in a sequential manner based on the validity of the active constraints. The proposed method can be applied with any objective function; however, in this paper, minimum generation cost is used as an objective function in the optimization problem. The method is demonstrated on several test systems including the IEEE RTS, IEEE 14, 30, 57, 118 and 300 bus test systems and the results showed the effectiveness of the proposed method against the traditional sensitivity analysis methods. Also, the results of the proposed method are validated using genetic algorithm.

Modeling and evaluating the capacity credit of PV solar systems using an analytical method

This paper introduces an analytical method to calculate the capacity credit of PV system in a manner that considers both the effect of input uncertainty and system components availability. The intermittency of the input source and system components availability were modeled separately and then convolved to construct a single capacity outage table. The discrete convolution method has been used in this work to build a generation model in the form of a capacity outage probability table. The proposed method is applied on the RBTS. The obtained results are compared to the results obtained by Monte Carlo simulation. The proposed method reduces the complexity of calculating the capacity credit of PV system considering the effect of system components availability.