Power Generation Demand Research Articles

Closed-Loop Tuning of Cascade Controller for Load Frequency Control of Multi-Area Distributed Generation Resources Optimized by ASOS Algorithm

This paper provides closed loop tuning of cascaded-tilted integral derivative controller (CC-TID) for load frequency control (LFC) of micro grid system. A micro grid system is the arrangement of distributed generation resources such as wind turbine generator (WTG), fuel cell (FC), aqua electrolyser (AE), diesel engine generator (DEG) and battery energy storage system (BESS). Different controllers such as proportional integral derivative (PID), two degree of freedom (2DOFPID), three degree of freedom (3DOFPID) and tilted integral derivative (TID) are used not only to sustain the disparity between real power generation and load demand but also accomplish zero steady state error to enrich the frequency and tie power regulations. The anticipated controller encompasses both the value of cascade (CC) and fractional order (FO) controls for better elimination of system instabilities. In the proposed CC-3DOFPID-TID controller, TID controller is castoff as a slave controller and 3DOFPID controller aided the role of dominant controller. The controlled parameters are optimized by adaptive symbiotic organism search (ASOS) algorithm for keen results of difficulties in LFC. To persist in ecosystem, symbiotic relations are predictable by organism through imitators. Further the dynamic behaviours of controller optimized by ASOS, teaching learning based optimization (TLBO) and differential evolution particle swarm optimization (DEPSO) are compared by extensive simulations in MATLAB/SIMULINK. Moreover the supremacy of proposed controller is performed through system dynamics comparison among PID, 2DOFPID, 3DOF-PID and CC-3DOFPID-TID controllers. Finally sensitivity of proposed controller has proven though random load perturbation.

Adaptive composite frequency control of power systems using reinforcement learning

With the incorporation of renewable energy, load frequency control (LFC) becomes more challenging due to uncertain power generation and changeable load demands. The electric vehicle (EV) has been a popular transportation and can also provide flexible options to play a role in frequency regulation. In this paper, a novel adaptive composite controller is designed to solve the LFC problem for the interconnected power system with electric vehicles and wind turbine. EVs are used as regulation resources to effectively compensate the power mismatch. First, the sliding mode controller is developed to reduce the random influences caused by the wind turbine generation system. Second, an auxiliary controller with reinforcement learning is proposed to produce adaptive control signals, which will be attached to the primary proportion-integration-differentiation control signal in a real-time manner. Finally, by considering random wind power, load disturbances and output constraints, the proposed scheme is verified on a two-area power system under four different cases. Simulation results demonstrate that the proposed adaptive composite frequency control scheme has a competitive performance with regard to dynamic performance.

Forecasting Electricity Power Generation of Pawan Danavi Wind Farm, Sri Lanka, Using Gene Expression Programming

This paper presents the development of a wind power forecasting model based on gene expression programming (GEP) for one of the major wind farms in Sri Lanka, Pawan Danavi. With the ever-increasing demand for renewable power generation, Sri Lanka has started harnessing electricity from wind power. Though the initial establishment cost of wind farms is high, the analyses clearly showcased the economic sustainability of wind power generation in long term. In this context, forecasting the wind power generation at Sri Lankan wind farms is important in many ways. However, limited research has been carried out in Sri Lanka to predict the wind power generation against the changing climate. Therefore, to overcome this research gap, a model was developed to forecast wind power generation against two climatic factors, viz. on-site wind speed and ambient temperature. The results showcased the robustness and accuracy of the proposed GEP-based forecasting model (with R2 = 0.92, index of agreement = 0.98, and RMSE = 259 kW). Moreover, the results of the study were compared against three different forecasting models and found comparable in terms of the model accuracy. The GEP-based model is advantageous over machine learning techniques due to its capability in deriving a mathematical expression. As an acceptable relationship was found between wind power generation and climatic factors, the proposed model facilitates the future projection of wind power generations with forecasted climatic factors. Though the application of GEP in the field of wind power generation is reported in a few research publications, this is the first research in which GEP is employed to model the power generation with respect to weather indices. The proposed prediction model is advantageous than machine learning models as the relationship between the wind power and the weather indices can be expressed.

The relation between internal flow and loss mechanism of unshrouded runners

Abstract High efficiency, low-cost, and reliable hydraulic turbines are in high demand for hydroelectric power generation, especially in medium and small-scale power plants. The authors’ laboratory has been developing unshrouded, i.e., band-opened runners of turbines to meet the demand. Those runners feature highly efficient performance and less expensive manufacturing costs produced by cutting, not by casting. For the sake of developing further improved unshrouded runners, it is necessary to understand their loss mechanism in detail. Therefore, in this study, the internal flow and the loss mechanism are investigated experimentally and computationally, with two different specific speed hydro turbine runners. First, Euler’s theoretical head on each runner is discussed to identify if they are influenced by the distinct specific speed. It demonstrated that the low specific speed turbine consumes greater potential energy in centrifugal force. Secondary, the loss mechanism in the unshrouded runners is investigated. The result showed that the dominant loss is caused by a tip leakage vortex developed by the collision of a leakage flow at the tip clearance and a secondary flow.

The Research on Condition Monitoring and Fault Diagnosis Method of Wind Power Spindle Bearing

With the continuous and substantial increase in the demand for wind power generation in China, the development of wind power generation units tends to be large-scale, intelligent and efficient. Besides, the working environment of spindle bearings is relatively bad and the failure rate is relatively high. Therefore, it is necessary to conduct state monitoring and fault diagnosis for wind power generation units. This paper first introduces the bearing system of wind turbine, then expounds the common faults of wind turbine spindle bearing, and carries out relevant research. According to the fault types, this paper investigates and combs the condition monitoring and fault diagnosis methods of spindle bearing. Finally, the author proposing a new condition monitoring method, which solves the vacancy of condition monitoring method based on strain.

Investigating the Complementarity Characteristics of Wind and Solar Power for Load Matching Based on the Typical Load Demand in China

This study explores the potential of renewable power to meet the load demand in China. The complementarity for load matching (LM-complementarity) is defined firstly. Kendall's correlation is employed to quantify the LM-complementarity. Then the complementarity characteristics on the hourly and daily time scales are analyzed. Results reveal that increasing the distance between interconnected power plants has weak improvements on the LM-complementarity in most cases. The LM-complementarity between wind and solar power is superior to that between wind or solar power generated in different regions. The hourly load demand can be effectively met by the LM-complementarity between wind and solar power. The optimal LM-complementarity scenario effectively eliminates the anti-peak regulation feature of wind power and reduces the phase differences between load demand and renewable power generation during a day. However, it is hard to balance renewable power generation and load demand on the daily time scale by the LM-complementarity. Compared with the complementarity for power smoothing defined in previous studies, the instability and peak-to-valley differences of the net-load demand can be effectively reduced by LM-complementarity. Thus the needs for flexibility are effectively reduced when integrating large-scale renewable power into the power system.

Heat‐Resistant Intermetallic Compounds and Ceramic Dispersion Alloys for Additive Manufacturing: A Review

Many industries such as aerospace, power generation, and ground transportation demand structural materials with high specific strength at elevated temperatures. Up until now, many types of heat‐resistant materials including Ni‐based superalloys, intermetallic compounds, and dispersion‐strengthened alloys have been developed for specific applications in these industries. Moreover, with the recent development of additive manufacturing techniques, these industries can now benefit from the rapid prototyping abilities, geometric freedom, and increased mechanical properties that can be achieved through various additive manufacturing processes. With this in mind, the progress made in additive manufacturing of heat‐resistant intermetallic compounds and ceramic dispersion alloys is herein examined. A brief introduction is provided on the target industries, applications, and the compositions of heat‐resistant alloys of current research interest. Then, recent research on heat‐resistant intermetallic compounds and ceramic dispersion alloys fabricated by additive manufacturing processes such as laser powder bed fusion, laser direct energy deposition, and electron‐beam powder bed fusion are reviewed with information provided on microstructure, processing parameters, strengthening mechanisms, and mechanical properties. Finally, an outlook is provided with future research suggestions.

Bidding Strategy of a Wind-Thermal GENCO considering Piecewise Linear AC Power Flow and Correlated Uncertainties

In deregulated electricity markets, generation companies (GENCO) try to maximize their economic benefits considering the electricity demand, transmission network condition, and other participants’ behaviors. The increasing penetration of renewable sources such as wind power generation with intermittent nature poses several challenges to the participation of GENCOs in the electricity market. Thus, this paper presents a stochastic bilevel optimization model to determine the coordinated bidding strategy of a wind-thermal GENCO with the aim of maximizing its profit in the day-ahead and real-time balancing market. Herein, the model aims to maximize the profit of GENCO in the day-ahead and the balancing market in the upper-level problem while minimizing the operation cost of the system in the lower-level problem. The uncertainties of wind power generation and electricity demand are modeled by defining a set of scenarios considering their mutual correlation using the copula technique. Additionally, incorporating AC power flow constraints in the proposed optimization model offers a better solution to the coordinated bidding strategy of the wind-thermal GENCO. Further, the nonlinear AC power flow equations are linearized using the piecewise approximation technique to reduce the computational complexity and enhance the accuracy of the optimal solution. In the end, the developed algorithm is implemented on the IEEE 24-bus RTS, and the simulation results are provided to validate the efficiency and applicability of the proposed coordinated bidding strategy model. The results advocate that the participation of the thermal unit along with the wind farm might mitigate the risk of uncertainties, but it causes an intense increase in the locational marginal price of the system. Importantly, the simulation results indicate the computational efficiency of the model by developing an exact AC power flow model without compromising the results. Notably, it has been found that the profit of the wind-thermal GENCO would be increased by 35.2% employing the copula technique to model the mutual correlation of uncertain parameters.

Numerical and Experimental Investigation of the Effect of Design Parameters on Savonius-Type Hydrokinetic Turbine Performance

To meet the increased demand of hydroelectric power generation, a novel drag-based Savonius turbine with the characteristics of a simpler fabrication process and good starting characteristics is designed, fabricated, and analyzed. The newly designed turbine is suitable to be installed in rivers, irrigation channels, ocean currents, etc., for small-scale hydroelectric power generation. In the present study, experiments are carried out to investigate the influence of the design parameters of this turbine on its power performance in order to improve its efficiency, including blade arc angles (180°, 135°), blade placement angles (0°, ±22.5°), and the number of blades (2, 3, 6, and 8). Further, three-dimensional CFD simulations are performed with Re = 6.72×105, matching the experimental conditions, in order to study the changes in the flow field and the rotation characteristics of the turbine. The research results indicate that a six-bladed turbine with a blade arc angle of 135° and a blade placement angle of 0° has higher torque and better power performance, which makes it the most suitable design when also considering cost. Furthermore, it was found that an increase in the number of turbine blades contributes to improving the performance of the turbine. The maximum power coefficient is 0.099 at a tip speed ratio of 0.34.

Optimization of the hybrid solar power plants comprising photovoltaic and concentrating solar power using the butterfly algorithm

This study optimized grid intermittency and instability resulting from photovoltaic (PV) by adding concentrating solar power (CSP) equipped with thermal energy storage (TES), which can be used to form hybrid solar power for grid dispatching. The PV–CSP were optimized by using a hybrid butterfly algorithm to meet the power generation demands and lowest system operation costs. Based on the optimal output and operating characteristics of hybrid power, the economics of peak-valley and fixed electricity prices were studied and compared. The optimal TES capacity was 6-h electricity generation. Hybrid power with optimal TES capacity could achieve a 4.2% increase for power production and 10.4% decrease for operation cost. The peak-valley price provided a better economic benefit than the fixed electricity price. A reasonable configuration of TES can ensure the flexibility and stability of hybrid plant. Hybrid power contributes to a flexible use of dispatch capabilities by equipped with TES, making PV power completely utilize, especially during the periods of high solar radiation. For power systems, hybrid power can be used to replace partly conventional thermal power to achieve highly economic and environmental benefits.

A framework to assess voltage stability of power grids with high penetration of solar PV systems

In recent years, grid integration of solar photovoltaic (PV) systems has proliferated across many countries in order to reduce greenhouse gas emission and minimize energy cost. However, the intermittency inherent within PV generator may affect the grid voltage stability significantly. Therefore, it is imperative to consider the intermittent nature of solar PV power generation and uncertainties associated with load demand to gain a clear insight on power grid voltage stability with high PV penetration. In this Writing – original draft, a framework is presented for power grid voltage stability analysis considering uncertainties of PV power generation and load demand using Monte Carlo simulation. The expected values of voltage stability indices such as critical eigenvalue, line loss, reactive power margin and loading margin have been determined in the proposed framework and their probability distribution have been obtained for different times throughout a day. The proposed methodology has been verified by analysing voltage stability of the modified IEEE 14 bus test system with high penetration of PV energy sources and considering uncertainties associated with load demand. The results provide a holistic understanding on voltage stability of the test system with different penetration levels of PV energy sources into the power grid during contingency as well as normal operating conditions.

Review of Hydrogen Production Techniques from Water Using Renewable Energy Sources and Its Storage in Salt Caverns

Hydrogen is becoming an increasingly important energy carrier in sector integration for fuel cell transportation, heat and electricity. Underground salt caverns are one of the most promising ways to store the hydrogen obtained from water electrolysis using power generation from renewable energy sources (RES). At the same time, the production of hydrogen can be used to avoid energy curtailments during times of low electricity demand or low prices. The stored hydrogen can also be used during times of high energy demand for power generation, e.g., with fuel cells, to cover the fluctuations and shortages caused by low RES generation. This article presents an overview of the techniques that were used and proposed for using excess energy from RES for hydrogen production from water and its storage techniques, especially in underground salt caverns, for the aforementioned purpose, and its feasibility. This paper compares and summarizes the competing technologies based on the current state-of-the-art, identifies some of the difficulties in hydrogen production and storage, and discusses which technology is the most promising. The related analysis compares cost and techno-economic feasibility with regard to hydrogen production and storage systems. The paper also identifies the potential, technical challenges and the limitations associated with hydrogen integration into the power grid.

A Hybrid Sailfish Whale Optimization and Deep Long Short-Term Memory (SWO-DLSTM) Model for Energy Efficient Autonomy in India by 2048

In order to formulate the long-term and short-term development plans to meet the energy needs, there is a great demand for accurate energy forecasting. Energy autonomy helps to decompose a large-scale grid control into a small sized decisions to attain robustness and scalability through energy independence level of a country. Most of the existing energy demand forecasting models predict the amount of energy at a regional or national scale and failed to forecast the demand for power generation for small-scale decentralized energy systems, like micro grids, buildings, and energy communities. A novel model called Sailfish Whale Optimization-based Deep Long Short- Term memory (SWO-based Deep LSTM) to forecast electricity demand in the distribution systems is proposed. The proposed SWO is designed by integrating the Sailfish Optimizer (SO) with the Whale Optimization Algorithm (WOA). The Hilbert-Schmidt Independence Criterion (HSIC) is applied on the dataset, which is collected from the Central electricity authority, Government of India, for selecting the optimal features using the technical indicators. The proposed algorithm is implemented in MATLAB software package and the study was done using real-time data. The optimal features are trained using Deep LSTM model. The results of the proposed model in terms of install capacity prediction, village electrified prediction, length of R & D lines prediction, hydro, coal, diesel, nuclear prediction, etc. are compared with the existing models. The proposed model achieves percentage improvements of 10%, 9.5%,6%, 4% and 3% in terms of Mean Squared Error (MSE) and 26%, 21%, 16%, 12% and 6% in terms of Root Mean Square Error (RMSE) for Bootstrap-based Extreme Learning Machine approach (BELM), Direct Quantile Regression (DQR), Temporally Local Gaussian Process (TLGP), Deep Echo State Network (Deep ESN) and Deep LSTM respectively. The hybrid approach using the optimization algorithm with the deep learning model leads to faster convergence rate during the training process and enables the small-scale decentralized systems to address the challenges of distributed energy resources. The time series datasets of different utilities are trained using the hybrid model and the temporal dependencies in the sequence of data are predicted with point of interval as 5 years-head. Energy autonomy of the country till the year 2048 is assessed and compared.

Boron‐containing nonwoven polymeric nanofiber mats as neutron shields in compact nuclear fusion reactors

Composite materials that contain thermally and chemically stable boron compounds are attractive candidates for applications that require high neutron absorption due to the large neutron absorption cross-section of boron. Electrospun polymer nanofibers have a unique volume-to-surface area, incorporating carbon and hydrogen atoms in their molecular structure that could moderate and thermalize incident neutrons. Nonwoven nanofibers could in turn reduce neutron scattering and increase neutron absorption by providing large surfaces and better atom economy in the shield materials. The expected high absorption will result from improved neutron interaction with high neutron absorbance hydrogen, carbon, and boron atoms in the nanofibers through increased specific surface area and atom economy. In this work, elemental boron-doped PVA polymeric nanofibers are produced for the first time to date for the application of neutron shielding. With the increasing demand for nuclear power generation and radiation therapies in medicine, the need for producing lightweight and flexible materials that could replace the traditional heavy metal/metal carbide-based shields is vital. Similarly, the need for flexible materials that could shield neutrons is also becoming critical for aerospace applications. The effect of boron content on the quality of fibers and neutron shielding capacity is evaluated by using an Am-Be neutron source. Characterization studies of produced nanofibers were carried out by SEM, TGA, FT-IR, and XPS analyses. Results have shown that PVA-based nanofibers doped with wt% 0.1 to 0.5 boron, having fiber diameter distribution between 60 and 330 nm range, could absorb up to 6% of neutron flux.

Optimizing diesel fuel supply chain operations to mitigate power outages for hurricane relief

Hurricanes can cause severe property damage and casualties in coastal regions. Diesel fuel plays a crucial role in hurricane disaster relief. It is important to optimize fuel supply chain operations so that emergency diesel fuel demand for power generation in a hurricane’s immediate aftermath can be mitigated. It can be challenging to estimate diesel fuel demand and make informed decisions in the distribution process, accounting for the hurricane’s path and severity. We develop predictive and prescriptive models to guide diesel fuel supply chain operations for hurricane disaster relief. We estimate diesel fuel demand from historical weather forecasts and power outage data. This predictive model feeds a prescriptive stochastic programming model implemented in a rolling-horizon fashion to dispatch tank trucks. This data-driven optimization tool provides a framework for decision support in preparation for approaching hurricanes, and our numerical results provide insights regarding key aspects of operations.

Fault Ride-Through (FRT) Behavior in VSC-HVDC as Key Enabler of Transmission Systems Using SCADA Viewer Software

The world’s energy consumption and power generation demand will continue to rise. Furthermore, the bulk of the energy resources needed to satisfy the rising demand is far from the load centers. The aforementioned requires long-distance transmission systems and one way to accomplish this is to use high voltage direct current (HVDC) transmission systems. The main technical issues for HVDC transmission systems are loss of synchronism, variation of quadrature currents, amplitude, the inability of station 1 (rectifier), and station 2 (inverter) to either inject, or absorb active, or reactive power in the network in any circumstances (before a fault occurs, during having a fault in network and after a fault cleared), and the variations of power transfer capabilities. Additionally, faults impact power quality such as voltage dips and power line outage time. This paper presents a method of overcoming the aforementioned technical issues using voltage-source converter (VSC) based HVDC transmission systems with SCADA VIEWER software and dynamic grid simulator. The benefits include having a higher capacity transmission system and proposed best method for control of active and reactive power transfer capabilities. Simulation results obtained using MATLAB validated the experimental results from SCADA Viewer software. The results indicate that the station’s rectifier or inverter can either inject or absorb either active power or reactive power in any circumstance. Also, the reverse power flow under different modes of operation can ride through faults. At a 100.0% power transfer rate, the rectifier injected 775.0 W into the network. At a 0.0% power transfer rate, the rectifier injected 164.0 W into the network. At a −100.0% rated power, the rectifier injected 1264.0 W into the network and direction was also changed.

Effect of Reynolds Number on Performance of Contra-Rotating Propeller Wind Turbines

In recent years, growing concern over global warming caused by the use of fossil fuels has led to a demand for clean power generation that can be used semi-permanently and is environmentally friendly. Wind power generation is expected as the development of new energy sources accelerates. Propeller wind turbines are widespread for large wind turbines. On the other hand, a wide variety exists for small wind turbines. Among them, the authors consider that 1kW wind power generation is important for the spread of small wind turbines, and are focusing on contra-rotating propeller wind turbines, which are expected to achieve high power. In this research, we aim to increase the power of a small wind turbine by installing a wind collecting device suitable for a contra-rotating propeller. However, the aerodynamic characteristics of a contra-rotating propeller inside a hollow structure have not been clarified. In addition, small wind turbines are operated under low Reynolds number conditions, and change in Reynolds number due to fluctuation in wind speed are expected to have a significant effect on performance. In this paper, the effect of Reynolds number on the performance of a contra-rotating propeller wind turbine is investigated by numerical flow analysis using a model with a contra-rotating propeller installed in the pipe.

Latest Technology for Power Generation Forecasting and Demand Forecasting

Latest Technology for Power Generation Forecasting and Demand Forecasting

Design of Battery Energy Storage System (BESS) with Fuzzy Control for Pico Hydro

Pico hydro system, a combination of hydro turbine governor, electronic load controller, and generator are outlined as one of the recommended approaches for off-grid power supply option for rural areas communities. In a conventional installation of a hydro system, an electronic load controller with proportional–integral–derivative (PID) is the best alternative to provide power balancing between power generation and load-consuming demand. However, there is always a peak in power demand during the day, but the energy consumption is low at night. This situation caused much energy to be dumped and wasted and a lack of energy management regarding the plant's power stability. Therefore, this research aimed to design fuzzy logic control (FLC) for an energy system that could meet the critical load requirement before being simulated using MATLAB SIMULINK to evaluate the effective utilization of the excess energy. Using Mamdani's methods with 25 membership rules to implement a fuzzy logic-based control system could potentially perform efficient power flow control between scenarios such as discharging, battery backup, and load supply. The result showed that this approach is a better alternative and more efficient system stabilization and energy supply by implementing fuzzy control in surplus power generation from the pico hydro system from 2 sec to 3 sec.

Finite element analysis of the flange with different fillet radius in tubular wind tower

Today’s modern world has more demand for electricity to perform most of the activities. In this regard, wind turbines are used to convert the wind energy into electrical power, where turbines are mounted at the top of the tower called wind tower. To meet the high power generation demand, the wind energy towers will have to become taller because winds are stronger at higher altitudes. In the present, work Modal analysis of the wind tower is done using ANSYS, and Campbell diagram is plotted to check the frequency of resonance between the tower natural frequency and the natural frequency of the rotating blades. Considering the different fillet radius in the flanges, the critical conditions are simulated under the same loading condition. Finite Element Analysis (FEA) results show that by increasing the fillet radius the Equivalent stress, Maximum principle stress and, Maximum shear stress decreases. The safety of the tower is ensured through the Campbell diagram where the excitation frequency of any blade is not crossing the exciter box.