Forced Outage Rate Research Articles

Implementation of Competitive Intelligence in Improving Company Performance of Power Plant in Indonesia

Numerous studies have been conducted on competitive intelligence and its impact on business performance. Most of companies agreed that competitive intelligence is a critical element of business and corporate strategy to increase income hence investment in competitive intelligence considered to be profitable. This research is aimed to expand the knowledge boundary by investigating the application of Competitive Intelligence in electrical power plant company. Twenty companies were selected as the target sample using purposive sampling design, in which the questionnaires were distributed to 120 individuals who were argued to have knowledge in the practice of Competitive Intelligence. In total, 105 questionnaires were returned for further analysis. Data were analyzed using descriptive statistic provided by SPSS. In the aspect of Competitive Intelligence Practices, results indicated that the mean score for each individual item range between 3.23 to 4.14, suggesting intensive practices of Competitive Intelligence. In the aspect of Utilization of Tools in practicing Competitive Intelligence, results indicated that three most widely used tools were from Employees, Customers and Internet. In the aspect of measurement variables in measuring competitive intelligence practices, results indicated that three most widely used measures were (1) yearly improvement of Equivalent Availability Factor-EAF (mean 3.96), (2) increase in percentage of planned maintenance (mean 3.82), and (3) yearly improvement of Equivalent Forced Outage Rate – EFOR (mean 3.75). Public utilities (infrastructure) company usually being pictured as companies that does not really care about competition as they usually being run by the Government. However, the findings of this research show otherwise

Risk Assessment of Power Systems Considering CVaR

In view of the randomness of component fault and load, considering the deficiency of traditional single reliability index, this paper introduces the Conditional Value-at-risk method based on Monte Carlo simulation, and constructs the probability model of risk assessment considering the forced outage rate of unit and line. In order to quantify uncertainty caused by random factors, the value-at-risk and conditional value-at-risk of power system security was proposed and used as a grid security index in the model combined with reliability index, which can accurately describe secure operation level in power system under different operation parameters, and reflect the severity of the accident and weakness and the grid, for further research on safe operation of power system to offer reference and decision-making basis.

A New Optimization Framework for Harmonic Compensation Considering Plug‐in Electric Vehicle Penetration Using Adaptive Particularly Tunable Fuzzy Chaotic Particle Swarm Optimization

Plug‐in electric vehicles (PEVs) can contribute to eliminating undesirable harmonics generated by nonlinear loads. In this study, a novel stochastic optimization approach for harmonic compensation is proposed which is capable of optimizing contrary objectives, including total harmonic distortion and harmonic inject current, simultaneously, while meeting the relevant constraints. This problem can be influenced by the uncertainty of PEVs which is reflected in the force outage rate concept. The Monte–Carlo simulation technique is implemented to consider the uncertainty associated with PEVs by generating plausible scenarios with the aim of converting the mentioned framework to the respective deterministic equivalents. Afterward, adaptive particularly tunable fuzzy chaotic particle swarm optimization (APTFCPSO) is utilized, based on the weighted sum method, and the acquired results are compared with those obtained by other implemented swarm intelligence‐based algorithms. Accordingly, at first, several benchmark optimization functions are considered to verify the performance of the APTFCPSO. Afterward, active power line conditioners (APLCs) and PEVs are separately employed for harmonics cancellation in the deterministic form. After adopting the scenario reduction technique, the optimization framework is solved for each remaining scenario by the mentioned procedure. The statistical analysis reveals that PEVs outperform APLCs to cancel harmonic orders defined in a 14‐node micro‐grid.

Cause-Consequence Diagram Reliability Analysis Using Formal Techniques With Application to Electrical Power Networks

Cause-consequence Diagram (CCD) is widely used as a deductive safety analysis technique for decision-making at the critical-system design stage. This approach models the causes of subsystem failures in a highly-critical system and their potential consequences using Fault Tree (FT) and Event Tree (ET) methods, which are well-known dependability modeling techniques. Paper-and-pencil-based approaches and simulation tools, such as the Monte-Carlo approach, are commonly used to carry out CCD analysis, but lack the ability to rigorously verify essential system reliability properties. In this paper, we propose to use formal techniques based on theorem proving for the formal modeling and step-analysis of CCDs to overcome the inaccuracies of the simulation-based analysis and the error-proneness of informal reasoning by mathematical proofs. In particular, we use the HOL4 theorem prover, which is a computer-based mathematical reasoning tool. To this end, we developed a formalization of CCDs in Higher-Order Logic (HOL), based on the algebraic approach, using HOL4. We demonstrate the practical effectiveness of the proposed CCD formalization by performing the formal reliability analysis of the IEEE 39-bus electrical power network. Also, we formally determine the Forced Outage Rate ( $\mathcal {FOR}$ ) of the power generation units and the network reliability index, i.e., System Average Interruption Duration Index ( $\mathcal {SAIDI}$ ). To assess the accuracy of our proposed approach, we compare our results with those obtained with MATLAB Monte-Carlo Simulation (MCS) as well as other state-of-the-art approaches for subsystem-level reliability analysis.

Evaluation of power plant reliability using index loss of load in the Suralaya power plant

Suralaya Power Plant (SPP) is the largest capable electricity generation company in Indonesia, with a total installed capacity of 3400 MW. As the largest power plant in Indonesia, SPP holds a great responsibility as a supplier 17% of the electricity needs of the Java, Madura and Bali (JAMALI) systems. This makes the SPP also one of the national vital objects (Obvitnas). This paper will calculate the reliability index of the generator using the method of loss of load probability (LOLP) and loss of load expectation (LOLE) based on the value of the force outage rate (FOR). Based on data on failure and daily expenses in 2019, the reliability index of SPP for units 1-4 is 4,23 days per year or 1,23%. The higher values LOLP/LOLE are in 2019 are possibility of SPP has lost its burden for 2 days in a period of 2019 because of force majeure (blackout) which causes a reduced load. To improve power plant is that the electricity load in West Java can be fulfilled with the electricity flow.

Economic evaluation of pumped storage power station based on multi-scenario stochastic Commitment Model

Pumped storage power stations have excellent characteristics such as fast start and stop speed, rapid load increase and decrease speed, and low forced outage rate, which can effectively improve the safety and economy of the power grid. This paper proposes a method for economic analysis of pumped storage based on a multi-scenario random unit combination model. In this paper, we build a scenario tree model based on the statistical characteristics of wind power and load forecast errors, and use a scenario modeling method based on associated nodes to build a stochastic unit combination model; Based on the results of system analysis, the economic efficiency of pumped storage power station is calculated, and the feasibility of the model is verified by example analysis, and the difference between calculation results of different models is also analyzed.

Optimal Planning Method for Distributed Wind/Solar/Battery Intergraded Microgrid

The high cost and randomness of intermittent renewable energy make the planning, integration and operation of power system more and more complicated. Therefore, determining the appropriate resource scale and related energy storage is the key to the efficient, economic and reliable operation of power system. In this study, two constraint based iterative search algorithms are proposed to optimize the scale of wind turbine (WT), solar photovoltaic (PV) and battery energy storage system (BESS) in microgrid grid connected configuration. The first algorithm is called resource size algorithm to determine the optimal size of resources; the second algorithm is called battery scale algorithm to determine the optimal capacity of BESS. These algorithms are mainly based on two key elements: maximum reliability and minimum cost. In this method, all possible solutions are searched in a given search space to avoid over and too small. At the same time, the forced outage rate of PV and the utilization rate of WT and Bess are considered to make it more practical. The simulation results show the effectiveness of the method.

Preventive Maintenance and Forced Outages in Power Plants in Korea

Maintaining high facility reliability in power plants is essential to secure long-term electricity supply. This paper applies the survival analysis to the actual unit level power generation data in Korea to estimate the relationship between facility reliability and the preventive maintenance. Duration of generators between forced outages is used to measure plant reliability. the empirical analysis shows that preventive maintenance cost, planned outage for maintenance, use rate, and reserve margin lead to the longer duration of generators and, in turn, the lower forced outage rates. We uncover that the marginal benefit of the preventive maintenance cost is decreasing at an increasing rate. It indicates that the marginal benefit of the “current” maintenance cost is minimal. Results in the paper imply that power plants in Korea might be spending unnecessarily high maintenance costs considering already having world’s lowest forced outage rates.

Resource adequacy implications of temperature-dependent electric generator availability

Current grid resource adequacy modeling assumes generator failures are both independent and invariant to ambient conditions. We evaluate the resource adequacy policy implications of correlated generator failures in the PJM Interconnection by making use of observed temperature-dependent forced outage rates. Correlated failures pose substantial resource adequacy risk, increasing PJM’s required reserve margin from 15.9% to 22.9% in the 2018/2019 delivery year. However, PJM actually procured a 26.6% reserve margin in this delivery year, translating to excess capacity payments of $315 million and an implied value of lost load of approximately $700,000/MWh, a figure two orders of magnitude greater than typically used in operational contexts. Capacity requirements vary by month, with more than 95% of loss-of-load risk accruing in July. Setting monthly capacity targets could reduce annual PJM procurement by approximately 16%. We examine the resource adequacy implications of the ongoing replacement of nuclear and coal in PJM with combined-cycle gas generators, finding moderate benefits: approximately a 2% reduction in capacity requirements. We identify modest resource adequacy risks from potential future climate scenarios, modeled as temperature increases of 1 and 2 °C relative to our study period. Holding loads fixed, these scenarios increase capacity requirements by approximately 0.5% and 1.5%, respectively.

Novel Modeling of Forced Outage Rate Effect on the LOLP and LOLE

Novel Modeling of Forced Outage Rate Effect on the LOLP and LOLE

Analysis the Impact of Renewable Energy Based-Wind Farms Installed with Electrical Power Generation System on Reliability Assessment

- Regard to the vast growing technologies in the use of wind turbines and wind farms as a renewable energy source, many searches had been proved its good impact on electrical transmission systems performance regarding congestion, power losses, voltage stability, and voltage profiles. This paper aims to study the effect of the connection of wind farms to the power system from the view of reliability and evaluates all the states of different generation’s probabilities for the power systems. The proposed method used both block diagrams and the Markov chain techniques to assess the whole generation system reliability, the generation buses reliabilities, the states of generation probabilities, the frequency and mean duration of generation failure states, and the capacities of generation's system that are in or out of service for each failure state. Also, it is assessed the system reliability, reliability indices, and analyzed the impact of forced outage rate for each wind turbine and the number of used wind turbines in each wind farm. MATLAB code is developed using Markov chain construction. The effectiveness of the proposed method is demonstrated through an updated version of IEEE_RTS_24_node with installed six wind farms (IEEE_UV_EPS_24_bus_6_WFs). Each wind farm has power capacity of 200 MW.

Power System Market Planning

The loss of load expectation (LOLE) considered an acceptable security standard across most of the national electricity systems. In the present study, the LOLE is calculated. This means that the LOLE needs developing a model. The developed model serving to assesses the security of supply risks associated with the different electricity capacity margin levels, where the capacity margin is the level by which available electricity generation capacity exceeds the maximum expected level of demand. Then, the developed model updated on an annual basis. The annual update is helping to fulfill the Electricity Authority for planning, operation and control. The developed model will assess the system by calculating the capacity margin probability by combining both the generation and load models. The calculation of LOLE is an internationally accepted criterion in capacity adequacy. The results for the model of each region vary due to several factors, such as generation resource, load forecast, and forced outage rates (FOR). The estimation of the risk is economically optimal reserve margin to a number of case studies assumptions considered in the present study. The results help in future planning for the electric system operation and control. Furthermore, the study is helping to evaluate the implications of the obtained results for the electricity policy market to determine the best model for market design in the future.

Power System Market Planning

The loss of load expectation (LOLE) considered an acceptable security standard across most of the national electricity systems. In the present study, the LOLE is calculated. This means that the LOLE needs developing a model. The developed model serving to assesses the security of supply risks associated with the different electricity capacity margin levels, where the capacity margin is the level by which available electricity generation capacity exceeds the maximum expected level of demand. Then, the developed model updated on an annual basis. The annual update is helping to fulfill the Electricity Authority for planning, operation and control. The developed model will assess the system by calculating the capacity margin probability by combining both the generation and load models. The calculation of LOLE is an internationally accepted criterion in capacity adequacy. The results for the model of each region vary due to several factors, such as generation resource, load forecast, and forced outage rates (FOR). The estimation of the risk is economically optimal reserve margin to a number of case studies assumptions considered in the present study. The results help in future planning for the electric system operation and control. Furthermore, the study is helping to evaluate the implications of the obtained results for the electricity policy market to determine the best model for market design in the future.

Co-optimization of Transmission Maintenance Scheduling and Production Cost Minimization

Regular transmission maintenance is important to keep the infrastructure resilient and reliable. Delays providing on-time maintenance increase the forced outage rate of those assets, causing unexpected changes in the operating conditions and even catastrophic consequences, such as local blackouts. The current process of maintenance schedule is based on the transmission owners’ choice, with the final decision of system operator about the reliability. The requests are examined on a first-come, first-served basis, which means a regular maintenance request may be rejected, delaying the tasks that should be performed. To incorporate optimization knowledge into the transmission maintenance schedule, this study focuses on the co-optimization of maintenance scheduling and the production cost minimization. The mathematical model co-optimizes generation unit commitment and line maintenance scheduling while maintaining N-1 reliability criterion. Three case studies focusing on reliability, renewable energy delivery, and service efficiency are conducted leading up to 4% production cost savings as compared to the business-as-usual approach.

Long-Term Generation Maintenance Scheduling with Integration of Pumped Storage Units

In a power system, the reliable performance of generation units for the supply of demand has the great importance. The execution of maintenance in a timely manner has a great impact on forced outage rate of generation units and so has impacts on mitigation of generation costs. In restructured power systems, regard to the independence of generation companies, the procedure of maintenance planning has become more complicated compared with traditional power systems. This study presents an optimization problem, which seeks for a long-term maintenance scheduling method for power generation units subject to the reduction of maintenance and operation costs, retaining the system's reliability and satisfaction of prevailing constraints. A mathematical method based on Benders decomposition technique is proposed to arrange a maintenance schedule for generation units. A penalty factor corresponding to the peak load curve is defined and the lost profits of the units at the time of maintenance are also added to the objective function, to schedule maintenance proportional to the consumption patterns. In addition, by integrating the hydro pumped storage unit into the power system, the reliability of the system has been improved. IEEE-RTS 24-bus test system and GAMS software are used to show the effectiveness of the proposed method. The obtained results imply the accuracy, simplicity and great performance of the model.

Evaluati on of long-term start up costs impact on short-term price based operational optimization of a CCGT using MILP

An increasing share of the weather-dependent RES generation in the power system leads to the growing importance of flexibility of conventional power plants. They were usually designed for base load operation and it is a challenge to determine the actual long-term cycling costs, which account for an increase in maintenance and overhaul expenditures, increased forced outage rates and shortened life expectancy of the plant and components. In this paper, the overall impact of start up costs is evaluated by formulating and solving price based unit commitment problem (PBUC). The electricity spot market is considered as a measure for remunerating flexibility. This approach is applied to a real-life case study based on the 70 MWe PGE Gorzόw CCGT power plant. Different operation modes are calculated and results are used to derive a mixed integer linear programming (MILP) model to optimize the operation of the plant. The developed mathematical model is implemented in Python within the frame of the PuLP library and solved using GUROBI. Results of the application of the method to a numerical example are presented.

Reliability, economic and environmental analysis of a microgrid system in the presence of renewable energy resources

The application of renewable energy resources in a power system has received significant attention owing to the environmental impacts and fluctuations of fossil fuel prices. Consequently, renewable energy resources have become important sources to generate power at the commercial level due to their various benefits, coupled with the government incentives and public supports. This research work is focused on the evaluation of the reliability, economic and environmental benefits of renewable energy resources in a microgrid system. The lifecycle analysis of a microgrid system that consists of the photovoltaic, wind turbine generator, electric storage system and diesel generator is implemented in this study to test their commercial prospects in rural communities that have no access to electricity due to economic and technical constraints. The objective of this research work is to minimize the cost of energy, lifecycle cost, the annual cost of load loss and lifecycle greenhouse gas emission cost as well as to improve the overall benefit of green technologies in the proposed microgrid system. This objective is achieved by utilizing the basic probability concept to obtain the reliability performance indicators such as expected energy not served, loss of load expectation and loss of load probability, in addition to utilizing an fmincon optimization tool in the MATLAB environment to investigate the environmental and economic effects of renewable energy resources in a power system. The suitability of the model is tested on six case studies by using the same load profile, wind speed and irradiation of the site and diesel generator power capacity. The market factors such as interest rate and price of diesel fuel as well as forced outage rate, annual peak load variation and distributed generation penetration level are utilized to study their impacts on the operation of a microgrid system. The results obtained in this study demonstrate the optimal feasibility of renewable energy resources in a microgrid system. This indicates that it offers a significant reduction in the values of lifecycle cost, cost of energy, greenhouse gas emission cost and the annual cost of load loss when compared with case study 1. This research work shows that the utilization of green technologies in a microgrid system optimizes the reliability, cost savings, lifecycle cost and environmental impact. The technique adopted in the study can serve as a reference for rural electrification projects and solve socioeconomic problems that are associated with power outages.

Resilience-Constrained Hourly Unit Commitment in Electricity Grids

This paper proposes a resilience-constrained unit commitment (RCUC) framework in which the system operation constraints, heterogeneity of power flow distribution and lines forced outages are simultaneously addressed. The proportional hazard model (PHM) is adopted to shape line forced outage rates with the cumulative effect of weather conditions and line loading rates. A sequential and Monte Carlo-based framework is established for RCUC using the recursive sampling process. The RCUC solution in each period affects the previously accumulated outage probabilities, thus it would have an impact on the outage process. In turn, the sampled line outages affect the RCUC solution in the next period and the loading of remaining lines. This is a sequential process in which the RCUC and the outage sampling of power system components are computed alternatively until the scheduling horizon is culminated. During each period, two penalty terms are introduced into the RCUC model to seek a tradeoff between operation cost and the homogeneity of flow distribution in power network and the loading rates of local lines affected by extreme weather. Since the penalty terms are modeled by absolute value functions, a general linearization method for absolute value functions in the mix-integer programming is presented. The overall convergence of the Monte Carlo technique is quantified by the coefficient of variation of total costs over multiple simulations. If the convergence criterion is not satisfied, the Monte Carlo solution approach will be continued. The case studies illustrate the effectiveness of the proposed RCUC model.

Reliability-Based Phasor Measurement Unit with Outage of Transmission Lines

This paper discussed on the Monte-Carlo simulation technique to determine the optimal placement of Phasor Measurement Unit (PMU) in power system whilst ensuring the observability of the system. In addition, the information on Force Outage Rate (FOR) of the system can be calculated using Markov Chain technique. The FOR represents the level of risk security for the transmission line that happened because of unscheduled and unexpected failure or repair in the system. Subsequently, the reliability model of the transmission line can be developed. Using IEEE 57-bus system, the results obtained from Monte-Carlo simulation technique demonstrate the optimal PMU placement, the desired reliability of the Wide Area Monitoring System (WAMS) as well as the number and location of covered contingencies of the system.

Evaluation of Generation System Reliability Using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANNs)

This paper presents an evaluation of the reliability index of power generation systems using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANNs) to compare the results obtained from the basic method of probability. The reliability index used in this study is the Expected Energy Not Supplied (EENS) index, which is used in planning to increase the installed capacity for the adequate demand for electricity. The ANFIS and ANNs techniques will learn the relationship between the priority level, the installed capacity and the force outage rate (FOR) of the generator, which significantly affect the EENS index. The results indicated that the ANNs techniques have the best predictive performance. The best accuracy of the training data was 1.2488% and the testing data was 2.3963%, calculated using a Mean Absolute Percentage Error (MAPE). Furthermore, the ANNs took more time to learn faster than the ANFIS.