Reliability Analysis using Renewable Energy Sources

Probabilistic techniques are widely used in power system reliability evaluation. The average or mean values of a wide range of indices are used to assess the reliability of generation, transmission and distribution systems. These mean values are extremely valuable and are the primary indices in generation adequacy studies of power systems containing both conventional generating units and unconventional energy sources such as wind and solar energy. Additional information can, however, be created, which can prove useful in a wide range of systems and applications. The probability distributions associated with the reliability indices can be used as supplemental measures and provide valuable information for system planners and operators. This paper presents the results of a series of studies on reliability index distributions for small isolated power systems using wind energy and storage facilities. Reliability index distributions associated with generating capacity adequacy parameters such as the loss of load expectation (LOLE), expected outage frequency (EOF), expected energy supplied by the battery (EESBB) and battery discharging frequency (BDF) etc. are presented and examined.

Wind power is an intermittent energy source that behaves quite differently from conventional energy sources. The reliability impact of this highly variable energy source is an important aspect that needs to be assessed as wind power penetration becomes increasingly significant. Generation adequacy assessment including wind energy conversion systems (WECS) at multiple locations is described in this paper. Effective load-carrying capabilities (ELCC) obtained using the loss of load expectation (LOLE) and the loss of load frequency (LOLF) for a power system containing WECS are illustrated and compared. The results show that ELCC obtained using the LOLF and obtained using the LOLE for WECS can be considerably different, while they are similar for a conventional generating unit. The impact on the system reliability indices of wind speed correlation between two wind farms is also examined. The studies show that the degree of wind speed correlation between two wind farms has a considerable impact on the resulting reliability indices. The sequential Monte Carlo simulation approach is used as this methodology can facilitate a time series modeling of wind speeds, and also provides accurate frequency and duration assessments. An autoregressive moving average time series model is used in this study to simulate hourly wind speeds

Generation planning utilizes reliability indices as criteria to ensure adequacy in terms of total installed capacity. Reserve Margin and Loss of Load Expectation (LOLE) are the most widely-used indices in generation adequacy evaluation. Reserve Margin is a measure of available capacity over and above the capacity needed to meet normal peak demand levels. In Peninsular Malaysia, the amount of Reserve Margin has been perceived to be high. Generally, high Reserve Margin can provide high reliability. However, it acquires more generation plant, for which some of them may not be necessary. This may indicate over investment which will be reflected in the tariff structure. LOLE is a probabilistic measure which indicates the risk at which the generation capacity fails to meet the demand and its evaluation involves specific parameters such as the plant capacity and outage rate of each generating unit. Therefore, in order to have optimum generation planning and investment efficiency, it is necessary to perform a study on the practical Reserve Margin level with respect to the current LOLE requirement without endangering the overall power system reliability. This research studies the factors affecting LOLE and evaluates the relationship between Reserve Margin and LOLE under various conditions. A modified Peninsular Malaysia system is simulated using Wien Automatic System Planning (WASP -IV) to determine LOLE focusing on thermal power plants. This study concludes that peak load and forced outage rate give significant impacts to the LOLE and thus, the reliability of the system. Effort to ensure availability especially during peak load may need to be intensified. The study also establishes an inverse exponential curve for the relationship between Reserve Margin and LOLE. It is found that the outcome of the study is to enhance generation planning decision making in obtaining the optimum Reserve Margin considering the LOLE under various conditions.

This article provides the reliability of wind energy, Due to the increasing use of wind power plants in the world and the susceptibility of the plant to check the reliability of the various factors seems necessary. In this paper, test network reliability indexes are examined in the presence of wind farms. Reliability indices LOLE (Loss of Load Expectation) and EENS (Expected Energy Not Supplied) examined in this article. Reliability analyses of power systems are conventionally done by using either the analytical method based on the contingency enumeration approach or Monte Carlo simulation. In this approach, the software tool Monte Carlo Simulation (MCS) is applied to simulate the behavior of the corresponding system. The Roy Bilinton Test System (Rbts) system is utilized to demonstrate the desired model.

The reliability study is one of the major factor study to design, operation and maintenance of power system. Therefore, the reliability indices are important to be calculated. Based on the target of the study, the index required is calculated. Therefore, in the present study, the Loss of Load Expectation (LOLE) is required to evaluate the system reliability. The LOLE is defined as the number of days per year or the number of hours per year. The calculation of LOLE needs to know the Forced Outage Rate of each generating-unit. Finally, this will help for the smart cities that need to minimize the LOLE.

Intelligent search based techniques such as genetic algorithm (GA) have been proposed to deal with reliability evaluation of complex power systems recently. In this type of methods, the guided search is carried out on a population scale trying to find all the dominant failure states, based on which different reliability indices can be calculated accordingly. However, the process may be time-consuming when power flow analysis is involved in deciding the status of a system state in complex power systems such as composite systems. To speed up the computing process, parallel implementation of GA is proposed in this study by using multi-deme based search, where multiple subpopulations are distributed in different processors. In this way, simultaneous search is achieved through parallel implementation. An IEEE reliability test system is used for simulation studies. It turns out that the proposed parallel method is effective in increasing the computing efficiency of GA when it is used for reliability evaluation of composite power systems.

Power system reliability evaluation considering cyber-malfunctions in substations

Risk index of loss-of-load expectation is traditionally used to determine generation adequacy. However, be-cause responsibility of generation installation and demand increases with different entities, both generation and load need to be examined to evaluate the resource adequacy of today’s deregulated power systems. An electric utility’s main concern is to plan, design, operate and maintain its power supply to provide an acceptable level of reliability to its users. This clearly requires that standards of reliability be specified and used in all three sectors of the power system, i.e., generation, transmission and distribution. Reliability indices have been defined for the three sectors separately as well as for the bulk power system. Reliability criteria may be determined at the selected load points in the system for different combination of generators and transmission line failures. This paper presents an effective simulation method for the reliability assessment of reliability index, loss-of-load expectation (LOLE). This method is applied to Bangladesh Power System (BPS). BPS has a total installed capacity of around 6550 MW. The maximum demand of BPS is about 5700 MW. The relevant data of the generators and hourly load profiles are collected from the National Load Dispatch Center (NLDC) of Bangladesh and reliability index ‘LOLE’ is assessed for the last six years.

With large-scale wind farms integrated into power systems, the computational requirements for reliability evaluation of composite power systems are increasing. This paper introduces a new concept of dynamic orthogonal list, which is a form of multiple failure retrievals to avoid repeatedly calling the optimal power flow (OPF) program in the stage of state evaluation. An existing algorithm of cross-entropy (CE) method was developed to deal with the rare events in sampling. The major contribution of CE is to achieve convergence faster and reduce the number of samples, thus shortening the consuming time in the stage of sampling. This paper proposes a fast reliability evaluation process which combines the CE method with dynamic orthogonal list (CE-DOL), comprehensively improving the computational efficiency of Monte Carlo simulation (MCS). The proposed method is tested on a modified IEEE-RTS 79 system. In addition, for comparison purposes, the efficiencies of MCS, CE, and CE-DOL are estimated in the cases of different system adequacies. It is shown that, in range of certain reliability level, the proposed method is much more efficient compared with the CE method. Finally, the applicability of different methods is analyzed under different system reliability levels.

Modern electric power systems are large and complicated, and, in many regions, the generation and transmission systems are operating near their limits. Eigenanalysis is one of the tools used to analyze the behavior of these systems. Standard eigenvalue methods require that simplified models be used for these analyses; however, these simplified models do not adequately model all of the characteristics of large power systems. Thus, new eigenanalysis methods that can analyze detailed power system models are required. The primary objectives of the work described in this report were I) to determine the availability of eigenanalysis algorithms that are better than methods currently being applied and that could be used an large power systems and 2) to determine if vector supercomputers could be used to significantly increase the size of power systems that can be analyzed by a standard power system eigenanalysis code. At the request of the Bonneville Power Administration, the Pacific Northwest Laboratory (PNL) conducted a literature review of methods currently used for the eigenanalysis of large electric power systems, as well as of general eigenanalysis algorithms that are applicable to large power systems. PNL found that a number of methods are currently being used for the this purpose, and all seem to work fairly well. Furthermore, most of the general eigenanalysis techniques that are applicable to power systems have been tried on these systems, and most seem to work fairly well. One of these techniques, a variation of the Arnoldi method, has been incorporated into a standard power system eigenanalysis package. Overall, it appears that the general purpose eigenanalysis methods are more versatile than most of the other methods that have been used for power systems eigenanalysis. In addition, they are generally easier to use. For some problems, however, it appears that some of the other eigenanalysis methods may be better. Power systems eigenanalysis requires the computation of eigenvalues of nonsymmetric matrices. Such computations are fairly difficult, however, and they constitute an area of active research. Thus, research in this area should be closely monitored, and, as new methods become available, they should be tested on large power systems. PNL also investigated the use of vector supercomputers to enlarge the size of power systems that can be analyzed by MASS, a standard power system eigenanalysis code. MASS was converted to run on a Cray supercomputer. On a conventional computer, MASS is limited to power system problems with about 500 states because of computer time constraints. Running MASS on a Cray X-MP EA/232, however, PNL found that a problem with about 2200 states could be solved in about 26 minutes. Furthermore, by moving to a larger Cray, it should be possible to use MASS to analyze power systems with 5,000 to 10,000 states. Problems with 5,000 states would probably take about 5 Cray hours, while a problem with 10,000 states would probably take about 43 Cray hours, though the actual execution times will depend on the type of Cray used. Problems requiring 5 Cray hours are not uncommon. Problems requiring 43 Cray hours, however, would be fairly expensive. Thus, power systems with about 5000 states probably represent an upper limit on the size of problems that one would want to routinely solve using a Cray version of MASS.

A convolutional neural network-based approach to composite power system reliability evaluation

The main objective of system reliability measures or indices is to quantify the behavior of the system related to its failures. This chapter describes some of the commonly used analytical methods in power system reliability evaluation. It provides a detailed description of the methods: State space method using Markov processes; Network reduction method; Conditional probability method; and Cut‐set or Tie‐set method. The state space approach is used to calculate reliability indices when all system states are described by a state‐transition diagram. Network reduction method can be applied for systems whose interactions among components can be easily captured by a block diagram called reliability block diagram. The main idea of cut‐set and tie‐set methods is to represent a reliability block diagram of a complicated structure by an equivalent one in a combination of series and parallel structures.

Operational scheduling of a smart distribution system considering electric vehicles parking lot: A bi-level approach

Sequence operation theory and its application in power system reliability evaluation

Day-ahead thermal and renewable power generation scheduling considering uncertainty