Power Plant Management Research Articles

PERFORMANCE MEASUREMENT TOOLS (PMT) FOR GAS ENGINE GENERATOR (GEG) POWER PLANTS: A CASE STUDY AT LEADING MEDIUM ENERGY COMPANY IN INDONESIA

Gas Engine Generator (GEG) Power plants as electricity producers need to be maintained using monitoring performance and continuously improvements. Company management of power plant is difficult to make decision for continuous improvement, requires Performance Measurement Tools (PMT) to determine the performance of a GEG power plant. PMT developed using Key Performance Indicator (KPI) that was defined through Forum Group Discussion (FGD) with different field of expertise in company and based on references and the weighting of KPI criterion that was developed by Analytic Hierarchy Process (AHP). This study was a case study at leading medium energy company in Indonesia. PMT is a tool to calculate Performance Score (PS) from the calculation of actual data, contracts and KPI (Power Output/PO, Heat Rate/HR, Operation Ratio/OR, Capacity Factor/CF) weights. If there is a gap in the PS then it can indicate penalty from customer especially PO and HR. Case study was conducted on three power plants that have different capacity and number of engines. The sum of the PS from the KPI will get a Total Performance Score (TPS). The highest TPS value will get a Performance Benchmark (PB) for each power plant.

Intelligent control algorithm for autonomous integrated power plants for Arctic regions

The article considers the information and technical aspects of an innovative autonomous integrated power plant management, including alternative energy sources and a diesel generator set, as well as controlled inverters that provide energy supply to consumers of various priority categories, which can be used in the Arctic regions of the Russian Federation. The main aspects of creating innovative systems are considered and it is determined that the creation of integrated energy systems requires a significant deepening of cooperation between national producers in order to ensure the scalability of integrated energy systems by ensuring the unity of information means of data exchange between individual modules and the control system. It is shown that a specific requirement for control systems of complex power plants is the requirement of high autonomy, including the ability to provide consumers with electricity under variable environmental conditions without direct intervention of operational personnel. The article substantiates the division of the information and algorithmic support of the control system of a complex power plant into two modules – analytical and control. For the analytical module, an algorithm is proposed that ensures the development of control solutions in a complex energy system, ensuring the stability of energy supply to the most important consumers. At the same time, the algorithm provides an increase in the reliability of the energy storage device based on Li-Ion batteries used in the system not only by eliminating excessive charge and deep discharge, but also by reducing the number of charge/discharge cycles. The solution of system autonomy problem is provided by a multivariate algorithm for predicting weather conditions using statistical data and methods for analyzing fuzzy time series. The intelligent control algorithm was implemented in C++, the weather forecasting algorithms were implemented in Python using the ANFIS library.

Сравнительный анализ критериев и методов определения тренда во временных рядах параметров регистрации технического состояния газотурбинных двигателей

The main directions of improving the processes of life cycle management of power and power plants based on gas turbine engines are based on the strategy of operation according to the technical state. The very concept of a technical condition and the need to assess the tendencies of its permissible change determine the use of applied statistics methods to establish such tendencies - trends. Trend analysis has now emerged as an independent direction in applied statistics due to the specifics of the research object and the importance of applied applications. The problematic issue of the implementation of the strategy for the operation of gas turbine engines according to the technical condition is the reasonable choice of such criteria and methods for determining the trend, which are most consistent with the objects of diagnostics. One of the most important tasks of time series analysis is to substantiate the statistical model of data generation. Such a model, or their combination, should adequately reflect the change in the properties of an object during long-term operation, take into account the features of power plants for aviation and ground use, as well as the experience of use according to the operation manual. The next task is to select and analyze the trend and randomness criteria in relation to the time series, consisting of the parameters for registering the technical state of gas turbine engines. The specificity of trend criteria lies in the fact that at a given level of significance, the hypothesis about the randomness of the time series can only be refuted. The study of an alternative encounters significant difficulties, since the presence of a trend turns a time series into a non-stationary random process. At the same time, the practice of application requires a rational combination of errors of the first (false alarm) and second (trend skipping) kind. Operation by technical condition creates the prerequisites for the use of modern trend analysis methods, which allow dividing the time series into components in accordance with the accepted statistical model of data generation. From the group of such methods, it is advisable to single out the methods of orthogonal decomposition (factor analysis, the method of principal components), since they allow making a predictive assessment of the development of a trend. However, the proposed methods are predominantly scalar in nature, while the initial data on the technical condition of gas turbine engines are multidimensional time series of output parameters related to each other. Comparative analysis of the criteria and methods for determining the trend in the time series of registration parameters is of great practical importance, since it allows increasing the reliability of statistical conclusions about the technical condition of gas turbine engines.

Некоторые аспекты реализации технологий управления энергетическими установками с учетом концепции автономных судов

Обеспечение удаленного управления судовой энергетической установкой автономного судна, контроль параметров процессов и оценка их технического состояния с использованием искусственных нейро-нечетких сетей для морского автономного надводного судна (МАНС). Создание цифровых двойников и прогнозирование технического состояния, моделирование сценариев изменения технического состояния судовых технических средств. Современные тенденции и перспективы научных изысканий в области управления судовыми энергетическими установками и системами, их обслуживающими, применительно к концепции автономного судна и безэкипажного (беспилотного) судоходства, а также выполнения ужесточающихся требований международного морского законодательства по предотвращению загрязнения атмосферы с судов. Использование альтернативных топлив и переход на электроснабжение с берега при стоянке таких судов в портах, на оборудованных рейдовых стоянках, а также использование «чистой электроэнергии» для электроснабжения объектов водного транспорта и шельфовой инфраструктуры. Providing remote control of the ship's power plant of an autonomous ship, monitoring process parameters and assessing their technical condition using artificial neuro-fuzzy networks for a marine autonomous surface vessel (MANS). Creation of digital twins and forecasting of the technical condition, modeling of scenarios of changes in the technical condition of ship technical equipment. Modern trends and prospects of ship power plants and their elements in relation to the concept of an autonomous vessel and crewless (pilotless) navigation, incorporating meeting the potential newer requirements / restrictions of international maritime legislation regarding airborne pollutions from ships. The use of alternative fuels and the alternative to marine power technology from the shore when such vessels are moored in ports, at equipped roadsteads, as well as the use of “clean electricity” to supply power to marine transport facilities and offshore infrastructure.

Application of Analytic Hierarchy Process (AHP) to Develop the Weighting of Key Performance Indicators on Gas Engine Power Plants

Power plants as electricity producers need to be maintained using monitoring performance and continuous improvements. Company management of power plant is difficult to make a decision for the continuous improvement, requires Performance Measurement Tools (PMT) to determine the performance of a gas engine power plant. PMT developed using Key Performance Indicator (KPI) that was defined through Forum Group Discussion (FGD) with different field of expertise in company and based on references and the weighting of KPI criterion that was developed by Analytic Hierarchy Process (AHP). This study has developed the weight of KPI that the weight of each criterion which will be used as a reference is Rank 1 – Power Output (MW) – PO (70.81%), Rank 2 – Heat Rate (Btu/kWh) – HR (14.91%), Operating Ratio (%) – OR (8.06%) and Rank 4 – Capacity Factor (%) – CF (6.22%).

Life-cycle cost model and evaluation system for power grid assets based on fuzzy membership degree

Life-cycle cost (LCC) theory can be effectively applied to improve the efficiency and quality of power plant equipment and asset management. However, specific aspects of the LCC calculation and evaluation model require further research for practical application. This paper proposes an LCC assessment model for the management of electric power plant equipment during its service life. A membership function method based on fuzzy logic is used to improve the allocation of modernization and overhaul projects to multiple equipment assets. An LCC assessment model and evaluation system for power equipment are proposed and successfully applied to the equipment and project management of a Guangzhou power plant in the China Southern Power Grid, providing a decision-making mechanism that facilitates efficient operation and optimal utilization of power plant equipment and assets.

Optimal integration of demand response programs and electric vehicles in coordinated energy management of industrial virtual power plants

Today, implementation of an efficient and economical energy management system (EMS) in industrial smart grids- given the rising trend in electrical load consumption and restrictions in ramping up the power plants' capacities- cannot be underestimated. Optimal planning avails itself of the exploitation manner of existing energy resources alongside appropriate employment of demand response (DR) programs and available electric vehicles (EV) in parking lots of immense industrial centers as storage sites in energy management systems in industrial smart grids have been seriously considered. This article introduces a new perspective in the simultaneous use of demand response loads and available electric vehicles in parking lots toward energy management of industrial virtual power plants (IVPPs) aiming at augmenting the system's profits as well as increasing the grid reliability under peak load conditions and cutting down on industrial centers load shedding. The objective function of the problem is formulated within the framework of short-term production planning for distributed energy resources (DER) and conventional ones in conjunction with DR programs and EV in such a way that the IVPPs' profits are maximized. In order to validate the performance of the proposed method, we have applied and tried on the second zone of IEEE-RTS standard grid. The outcomes accrued from various simulations point to the fact that the presence of EV within the boundaries of EV's parking lots prompts considerable increase in IVPPs' storage amount and consequent decline in exploiting full power grid capacity. Utilizing DR programs for responding load and selecting best scheduling schemes for each IVPP during various times of a day-night cycles will bring about a reduction in operation costs, de-peaking as well as an enhanced performance of EV's parking installations.

SOLAR LOCATION ESTIMATION USING LOGSIG BASED ACTIVATION FUNCTION USING ARTIFICIAL NEURAL NETWORK APPROACH

Solar panel is one of the renewable energy that can reduce the environmental pollution and have a wide potential of application. The exact solar prediction module will give a big impact on the management of solar power plants and the design of solar energy systems. This paper attempts to find the best Artificial Neural Network (ANN) based logsig transfer function and various training algorithm that can be used to calculate the temperature module (Tm) in Malaysia. This can be done by simulating the collected data of four weather variables which are the ambient temperature (Ta), local wind speed (Vw), solar radiation flux (Gt) and the relative humidity (Rh) into the Neural Network Tool in MATLAB. Three different ANN transfer function and 14 types of training were compared to choose the best method. Finally, an equation for the ANN model will be generated in order to calculate the temperature module based on ambient temperature, local wind speed, solar radiation and relative humidity variables.

Electrolyzer-fuel cell combination for grid peak load management in a geothermal power plant: Power to hydrogen and hydrogen to power conversion

This study provides comprehensive energy, exergy, and economic evaluations and optimizations of a novel integrated fuel cell/geothermal-based energy system simultaneously generating cooling and electricity. The system is empowered by geothermal energy and the electricity is mainly produced by a dual organic cycle. A proton exchange membrane electrolyzer is employed to generate the oxygen and hydrogen consumed by a proton exchange membrane fuel cell utilized to support the network during consumption peak periods. This fuel cell can be also used for supplying the electricity demanded by the network to satisfy the loads at different times. All the simulations are conducted using Engineering Equation Solver software. To optimize the system, a multi-objective optimization method based on genetic algorithm is applied in MATLAB software. The objective functions are minimized cost rate and maximized exergy efficiency. The optimum values of exergy efficiency and cost rate are found to be 62.19% and 18.55$/h, respectively. Additionally, the results reveal that combining a fuel cell and an electrolyzer can be an effective solution when it comes to electricity consumption management during peak load and low load periods.

Effects of information strategies on public acceptance of nuclear energy

This study identifies the predictors of public acceptance of nuclear power plants. A comparative study of two types of information strategies, namely, interest focused and technology focused, is conducted to examine the decision-making process involved in the formation of nuclear power perception. Empirical results show that environmental concern and energy shortage belief are the key determinants of psychological perception and public acceptance. Innovatively, the respondents in the interest-focused group exhibit a lower acceptance level and fewer perceived benefits but more perceived risks than those in the technology-focused group. The technical information strategy can overcome the disadvantages of the interest-focused strategy by strengthening the negative effects of energy shortage belief and environmental concern on perceived risks and increasing people's openness to nuclear power plants. The belief–perception–attitude model from theory of planned behavior and the protective action decision model emphasizes the scientific value of external information and internal psychology to attitude changes. Thus, nuclear power plant managers are encouraged to promptly release professional messages and use a technical information strategy to win public support.

A Method to Estimation of Global Solar Radiation with Meteorological Parameters under Cloudless Sky Condition using Artificial Neural Network

In this work, variation of global radiation hourly basis and model developed to estimate the radiation under cloudless sky condition were purposed at Lopburi province (14.83°N, 100.62°E). Data of global radiation and meteorological parameters from 2012 to 2017 were investigated. For the variation of global radiation, the maximum and minimum of radiation are 3.46 MJ/m2 in April and 2.40 MJ/m2 in December, and there are some meteorological parameters influencing on the radiation. Therefore, a model for estimation of hourly global solar radiation under cloudless sky condition at this site was proposed based on the artificial neural network (ANN). This ANN has one input layer, two hidden layers and one output layer. The input layer consists of some meteorological parameters that are solar zenith angle, visibility, air temperature, relative humidity, wind speed and air pressure, and the output layer is global solar radiation under clear sky condition. The ANN was trained using the input and output data collected at Lopburi meteorological station during the year: 2012-2014. It was then validated against an independent data at the site for the period of three years (2015-2017). The validation result indicates that the estimated solar radiation under clear sky condition obtained from ANN are in good agreement with that from the measurement, with root mean square difference (RMSD) of 8.52% and mean bias difference (MBD) of 1.22%. Therefore, the model can be applied for estimation of global radiation under cloudless sky condition at other meteorological stations with similar climate. The estimated solar radiation data are useful for management in solar power plant, solar thermal energy system and also for the studies in the atmospheric field. Keywords: solar radiation, meteorological parameter, artificial neural network

Stand-Alone Hybrid Power Plant Based on SiC Solar PV and Wind Inverters with Smart Spinning Reserve Management

Stand-alone hybrid power plants based on renewable energy sources are becoming a more and more interesting alternative. However, their management is a complex task because there are many variables, requirements and restrictions as well as a wide variety of possible scenarios. Though a proper sizing of the power plant is necessary to obtain a competitive cost of the energy, smart management is key to guarantee the power supply at a minimum cost. In this work, a novel hybrid power plant control strategy is designed, implemented and simulated under a wide variety of scenarios. Thereby, the proposed control algorithm aims to achieve maximum integration of renewable energy, reducing the usage of non-renewable generators as much as possible and guaranteeing the stability of the microgrid. Different scenarios and case studies have been analyzed by dynamic simulation to verify the proper operation of the power plant controller. The main novelties of this work are: (i) the stand-alone hybrid power plant management regarding a battery energy storage system as a part of the spinning reserve, (ii) the characterization of the largest loads as non-priority loads, (iii) the minimization of the needed spinning reserve and fuel consumption from diesel generators.

The Small Hydropower Plant Income Maximization Using Games Theory

This paper is devoted to the consideration of case of the use of small rivers energy, the current system of support relevant projects, its drawbacks and opportunities to remove them. Cooperative game theory approach is used for analysis of regime management of the small-scale hydro power plant (SHPP). Technical and economical aspects of the issue are observed. The obtained results demonstrate the validity of the cooperation for obtaining additional income.

Transient and troubleshoots management of aged small-scale steam power plants using Aspen Plus Dynamics

This study is motivated by the insufficient studies on the dynamic simulation, operation control optimization, and operation strategies for small-scale old plants. In such small power plants, there is a need for cautions and stability during various troubleshoots. This study aims at developing a simulation model that accurately predicts the behavior of small-scale real plants under various part loads and troubleshoots. The actual operation of a 110-MW steam power-plant was modeled using Aspen Plus Dynamics, which employs a single-input single-output proportional integral (PI) controller. The controller tuning constants, K and IT, were optimized, and operation strategies for handling five troubleshoots are proposed. The simulation results are in good agreement toward the measured values, with a maximum relative error of 2%. The recommended operation strategies could prevent plant trip and return to steady state in a maximum of 5 min when two different halts are mimicked. Full-load thermal efficiencies of 94%, 97.9%, 92.6% were achieved when four burners were shutdown, high-pressure feedwater heaters tripped, and all the heaters tripped, respectively. The simulation presents the possibility of stabilizing the old power plants operation with maximum possible efficiency under various conditions. An updated operation and troubleshoot manual can be provided for various circumstances.

Social Scientists in an Adversarial Environment: The Nuclear Regulatory Commission and Organizational Factors Research

This paper examines the Nuclear Regulatory Commission’s (NRC’s) pursuit of social science research that could inform the oversight of nuclear power plant management. Perhaps no nuclear regulator has been as supportive of research on the intersection of organizational factors and reactor safety or as cautious in applying those findings to its regulations. This dissonance was rooted in the NRC’s long-held conviction that it should regulate power plants not people, which conflicted with its regulatory experience after the 1979 Three Mile Island accident (TMI). Intrusive oversight of a licensee’s “business,” it was believed, would destroy its sense of ownership for safety. TMI challenged that understanding of the NRC’s role, and a series of mishaps at other plants compelled the agency to cross the line between regulation and management. The NRC’s relationship with industry became highly adversarial, and the agency turned to social scientists to help establish an objective basis to judge a licensee’s organizational culture. Behavioral experts joined plant oversight review teams and received generous funding to quantify the contribution of organizational factors to accident risk. Scores of scholars at national laboratories and a dozen universities contributed, but the NRC abandoned the research in the mid-1990s in the face of inconclusive research and industry resistance. In need of a less controversial oversight program, the NRC abandoned direct assessment of plant management for a more quantitative approach that relied on plant performance indicators. When the 2002 Davis-Besse vessel head erosion event came perilously close to a significant loss-of-coolant accident, it raised questions about the appropriate role for the NRC in assessing a licensee’s safety culture. The NRC revised its oversight program to incorporate qualitative insights from its earlier research while still acknowledging the line between regulation and management. The NRC learned that while there were substantial cultural and technical obstacles to integrating safety culture insights with established management and regulatory practices, it was necessary to overcome them. The agency found stability in its contentious oversight program only when it made appropriate room for safety culture expertise.

AI-Guided Reasoning-Based Operator Support System for the Nuclear Power Plant Management

The decision-making process in the Nuclear Power Plant (NPP) control room faces some challenges: operator incomplete knowledge, insufficient time for responding to the highly dynamic events, and a large number of indicators to monitor. Because of the complexity of the NPP system, it is hard to pre-plan all the failures/mitigative actions. An intelligent operator support system is vital to mitigate these shortcomings. In this paper, an AI declarative approach (Answer Set Programming (ASP)) is employed to represent our knowledge of the nuclear power plant in the form of logic rules. This represented knowledge is structured to form a reasoning-based operator support system. When an incident occurs, this ASP-based reasoning support system is demonstrated to be capable of fault identification (diagnosis), informing the operator of different scenarios and consequences, and generating the control options (decision making).

Industrial tests of co-combustion of alternative fuel with hard coal in a stoker boiler

ABSTRACT This paper presents the results of industrial research on co-combustion of solid recovered fuel (SRF) with hard coal in a stoker boiler type WR-25. The share of SRF in the fuel mixture was 10%. During the co-combustion of SRF, no technological disturbances of the boiler were noted. The obtained SO2 and NOX emissions were comparable with coal combustion, but dust emissions increased. During the co-combustion of the coal mixture with 10% of alternative fuel, acceptable standards for co-incineration of waste were exceeded for NOx, dust, CO, HCl, HF, heavy metals, dioxins, and furans. The by-products of waste co-combustion with coal were non-hazardous waste. The obtained results constitute a very important contribution to the process of boiler retrofitting toward a waste co-incineration unit, and to meeting the legislative and environmental requirements. Implications: Due to some challenges related to waste storage and transportation, combustion in incineration plants and Waste-to-Energy plants is not possible. The adaptation (formal and technical) of medium scale boilers as co-incineration plants reveals high potential. Nevertheless, the lack of experience and investigations of waste co-combustion in real industrial scale grate boilers is observed. Thus, the implication of this article results consists of the investigations using industrial scale mechanical grate boiler (different from incineration type). Moreover, the investigations were carried out in a low-capacity boiler (~50% of nominal capacity). This novel experience is very important because reduced heat dissipation into the grid caused by high ambient temperatures occurs very frequently. These tests are valuable from the point of view of retrofitting the unit to obtain technological and emission parameters that would allow obtaining the status of a waste co-incinerating unit. The results of these investigations are addressed to power plant management board and engineering staff.

Management of the design and construction process of a thermal power plant under conditions of permafrost instability

The key issues of the process of the design and construction of a thermal power plant and the life cycle management of a thermal power plant under conditions of permafrost instability are considered. The article aims to develop information, mathematical and software support for the design and construction process supervising and testing of construction objects of a thermal power plant and life cycle management of a thermal power plant under conditions of permafrost instability. The following tasks arising in the process of achieving the given goal are presented: to study the characteristics of the constructed thermal power plant; to present the design and construction process of a thermal power plant in the form of an alternative graph; to identify alternatives and risks in the life cycle management system; to develop an investment project for a thermal power plant located in the Arctic region of the Russian Federation and to choose an effective option for the project implementation. It is shown that the design and implementation of projects for the construction of thermal power plants under conditions of instability of permafrost is characterized by the complexity of planning, large costs and a large number of operations. The practical significance of the work is determined by the possibility to apply the presented procedure for managing the design and construction process of a thermal power plant under conditions of permafrost instability when it is necessary to construct new thermal power plants in the Arctic region of the Russian Federation.

Study on the Development of I&C Equipment Performance Monitoring Program for Nuclear Power Plants

The equipment performance monitoring program is designed to provide guidance for monitoring the performance of equipment, with an aim to enable engineers to identify hidden hazards as early as possible and take corresponding corrective actions to achieve the purpose of equipment reliability management of nuclear power plant. The nuclear power plant realizes monitoring, control and protection of the whole plant through I&C equipment, by which to ensure the safe, reliable and economic operation of the nuclear power plant. In this paper, a set of performance monitoring program process special for I&C equipment of nuclear power plant is developed. And the RGL system (rod control system and rod position system) of a nuclear power plant is taken as an example to illustrate, analyze and summarize, which lays a foundation for nuclear power plant to carry out equipment reliability management.

A Study of Knowledge Management on Enhancing the Effectiveness of Plant Life Management for Nuclear Power Plants

A Study of Knowledge Management on Enhancing the Effectiveness of Plant Life Management for Nuclear Power Plants