73 research outputs found
Smart grid-integrated control of PV inverters for active voltage regulation and DER ancillary services
Full text linkThe increased penetration of grid-connected roof-top photovoltaic (PV) systems
distribution feeders is leading to technical issues to electric utilities, mainly voltage
fluctuations and violations to the grid codes limits. Furthermore, the legacy voltage
control devices such as transformer’s on-load tap changers (OLTC) and switched
capacitor banks need to be operated more frequently than usual with PV penetration and
could only alleviate slow-moving voltage fluctuations.
Ancillary services from Photovoltaic (PV) inverters can alleviate the impact of the rapidly
growing PV penetration by increasing distribution system flexibility and addressing
voltage regulation issues. However, the required communication infrastructure and
smart grid integration challenges limit broad deployments of PV ancillary services. This
thesis proposes a cost-effective volt/var (VVC) control scheme of multi-string PV
inverters for active voltage regulation and reactive power ancillary services using the
existing smart distribution infrastructure to avoid upfront costs of PV ancillary services.
The proposed VVC model is developed in MATLAB/Simulink to adapt PV reactive
power compensation according to X/R characteristics of the distribution feeder for
effective voltage regulation. A volt/var optimisation model is proposed based on particle
swarm optimisation to coordinate the reactive power dispatch of PV inverters with the
legacy voltage regulation devices to resolve the operational issues of distribution feeders
at high PV penetration levels.
Transient voltage analysis and quasi-static time series (QSTS) simulation analysis are
conducted using Matlab/Simulink and OpenDSS co-simulation environment.
A porotype is designed based on the compiled Simulink PLC code and verified in
CODESYS IEC 61131-3 standardization tool to address the practical considerations of
controlling multi-string PV systems and smart grid integration challenges. The proposed
VVC scheme is implemented using remote terminal units (RTU) in a real-world PV
system with multi-string inverters for experimental validation. Quasi-static time-series simulation and experimental results demonstrate the validated
effectiveness of the proposed control scheme in controlling fast PV fluctuations, resolving
voltage issues, voltage flickers, and enabling higher PV penetration. Furthermore, the
proposed VVC control algorithm of PV inverters led to operational benefits for utilities
by alleviating voltage flickers, reducing feeder losses, and significantly reducing regulator
switching operations (up to 37.1% saving in SCB operations and 34.7% saving in OLTC
operations), thereby extending their lifespan and potentially deferring the investment in
new voltage regulators.
The techno-economic assessment showed that the proposed VVC ancillary services
achieved significant improvements of the benefits-cost ratio (BCR) and net present value
(NPV) economic metrics of the system. The proposed BCR value increase by 13.3% from
1.27 to 1.44 when the proposed VVC was implemented. The respective (NPV) increased
from 62,117.86 due to the additional benefits achieved from the proposed
VVC ancillary services. Additionally, the payback period was reduced to 8 years using
the proposed VVC compared to 12 years with the existing P/Pn method.Heriot-Watt University scholarshi
Techno-Economic Analysis of a Residential PV-Storage Model in a Distribution Network
Full text linkThe high penetration level of photovoltaic (PV) generation in distribution networks not only brings benefits like carbon savings, but also induces undesirable outcomes, like more harmonic components and voltage fluctuations. Driven by decreasing costs of energy storage, the focus of this paper is to investigate the feasibility of applying energy storage in the grid-connected PV system to mitigate its intermittency. Firstly, to appreciate the functionality of storage, a generic PV-battery-supercapacitor model was simulated in MATLAB/Simulink, and a flat load profile was obtained to enhance predictability from the network management point of view. However, the usage of supercapacitors at the residential level is limited, due to its high startup costs. Secondly, a detailed residential PV-battery model was implemented in the System Advisor Model (SAM) based on local data in Dubai. The optimal sizing of a battery system was determined by assessing two criteria: The number of excursions, and average target power, which are contradictory in optimization process. Statistical indicators show that a properly sized battery system can alleviate network fluctuations. The proposed sizing method can be also applied to other PV-storage systems. Finally, economic studies of PV-battery system demonstrated its competitiveness against standalone PV systems under appropriate tariff incentives
Robust sliding mode controller for buck DC converter in off-grid applications
Full text linkThis paper presents a robust sliding mode controller of DC-DC buck converter for renewable energy applications, such as photovoltaic systems in off-grid configurations. Photovoltaic systems in off-grid configuration are exposed to significant variations in input voltage and power loads. The proposed sliding mode controller presents a simple and efficient method of continuously updating the duty cycle of a pulse width modulation unit (PWM) of a buck converter. The PWM unit is operated at constant switching frequency of 10 kHz carrier signal and varying duty cycle. The differences in input voltage and power load are treated as two bounded uncertainties, thus eliminating the need for input voltage sensor and output current sensors leaving the system with a single sensor required to measure the converter output voltage. That is, measured output voltage is compared with the reference voltage to continuously update the average duty cycle value of PWM unit. Adjustment of PWM duty cycle is performed while maintaining the sliding condition always fulfilled. The simulation results of the proposed controller showed robustness and accuracy against power load fluctuation, changes in desired output voltage, and variations in the input supply voltage that may result from the varying level of irradiance and temperature.</p
Design and analysis of sustainable photovoltaic solar charging system with battery storage for electric vehicles
Full text linkThis paper presents a sustainable electric vehicle (EV) charging system that operates in three modes of operation to maximize the yield of photovoltaic (PV) system. The design and analysis of the EV charging system is customized based on the operational or office hours of a corporation. The proposed system incorporates a battery pack capable of providing at least one day of autonomy to overcome the weather conditions during early morning, shading times, or cloudy days. In this study, the perturb and observe (P&O) algorithm is modified and used to operate the PV system at maximum power point (MPP) when charging either the EV or the storage battery. The load current, in both cases, is regulated using proportional integral (PI) controllers and pulse width modulation (PWM) switching of DC-DC converter. The proposed system operation is switched between three modes (boost operation for direct charging of EV and discharging of storage battery, and buck operation for charging of storage battery) by a simple event-driven finite state machine (FSM). Simulation results showed excellent tracking behavior of the proposed system when supplying a 5 kW load with variation in solar irradiance between 1000 and 400 W/m2, battery state of charge (SOC) between 40% and 100%, and temperature between 15 to 39 °C.</p
Energy Recovery from Low Speed Winds
No full textThis paper presents a renewable energy model which combines the concepts of wind pumps, pumped hydro and micro hydroelectricity. The proposed model recovers energy from low speed winds and eliminates the requirement of energy storage in batteries or grid connection. After establishing the suitability of the model theoretically, workable models are visualized using commercially available components. Two designs based on this model are studied for their performance, and resultant data has been critically analyzed to establish the trend and their commercial feasibility. One of the two designs studied is a standalone tower that can feed a house or a small villa, which also can be installed at any point of need. The second design is building integrated where the system components are mounted on a building. Theoretical findings are validated by simulating through software, MATLAB. The obtained results showed the superiority of the proposed model over the conventional three blade wind turbines for wind speed less than 6 m/s
Investigation of Voltage and Frequency Variation on Induction Motor Core and Copper Losses
No full textThis paper presents a dynamic induction motor(IM) model which incorporates all the power losses. Thepresented module is entirely built in Simulink to investigate theeffect of varying the applied voltage and frequency on IMefficiency for different load applications. The model includes thepower losses such as copper losses, core losses, stray load andmechanical. The accurate determination of induction motorefficiency depends on the estimation of all above mentionedpower losses which are modeled and presented in this paper. Theeffect of variation in applied voltage and frequency on inductionmotor efficiency is investigated at various load conditions and theresults are tabulated and evaluated accordingly. The obtainedresults show that the efficiency of the IM is significantly affectedby the voltage and frequency levels especially at low load.Therefore matching the right applied voltage and frequency tothe motor terminal based on the load condition will reduce themotor losses and hence increase its efficiency
Optimizing the Operation of Grid-Interactive Efficient Buildings (GEBs) Using Machine Learning
No full textThe building sector constitutes 40% of global electric energy consumption, making it vital to address for achieving the global net-zero emissions goal by 2050. This study focuses on enhancing electric load forecasting systems’ performance and interactivity by investigating the impact of weather and building usage parameters. Hourly electricity meter readings from a Texas university campus building (2012–2015) were employed, applying pre-processing techniques and machine learning algorithms such as linear regression, decision trees, and support vector machines using MATLAB R2023a. Exponential Gaussian Process Regression (GPR) showed the best performance at a one-year training data size, yielding an average normalized root mean square error (nRMSE) value of 0.52%, equivalent to a 0.3% reduction compared to leading methods. The developed system is presented through an interactive GUI and allows for prediction of external factors like PV and EV integration. Through a case study implementation, the combined system achieves 12.8% energy savings over a typical year simulated using ETAP 22 and Trimble ProDesign software version 2021.0.19. This holistic solution precisely models the electric demand management scenario of grid-interactive efficient buildings (GEBs), simultaneously enhancing reliability and flexibility to accommodate diverse applications
Sensorless Adaptive Fuzzy Logic Control Of Permanent Magnet Synchronous Motor
Full text linkPermanent Magnet Synchronous Motors (PMSM) require an electromechanical rotor
position sensor to operate. The rotor position sensor has disadvantages, such as
reliability, size, higher cost, and increased electrical connections. PMSM is used in
many speed and position control industrial applications. Proportional integral (PI)
and proportional integral derivative (PID) controllers have been widely utilised as
speed controllers in PMSM drives. However, these controllers are very sensitive to
step change of command speed, parameter variations and load disturbance.
In this work, an adaptive fuzzy logic speed controller is proposed. The main features
of the proposed controller are; quick recovery of motor’s speed from load
disturbances and insensitivity to parameter variation over a wide speed range.
The proposed controller is a hybrid model reference adaptive speed controller
(HMRASC) which mainly consists of two functional blocks. The first block is a
direct FLC that has the error and the change of error as inputs. The error signal is
measured between the actual motor speed and the desired speed and the output is the change in the torque command. The second block implements a model reference
adaptive controller. In the proposed system, the output speed of the reference model
is compared with the actual speed of the motor and the resulted speed error is applied
to a PI controller. The output signal of the PI controller is added to the direct FLC
output to compensate any deviations in the motor speed from the reference speed due
to parameters variation and disturbances in the load.
The design and optimisation of the FLC are carried out using an adaptive fuzzy
inference system network that uses the backpropagation, least square and gradient
algorithms. The fuzzy inference system is trained and designed using an adaptive
network. The rules and the implication method used are also optimised and
minimised in order to shorten the computation time. In addition, the effect of
different types and distributions of the membership functions were investigated and
presented.
This work also presents the estimation of the rotor position, which works effectively
with nearly zero estimation error over wide speed range, to replace the
electrometrical rotor position sensor. An estimation method based on the back EMF
and flux estimation is presented to calculate the rotor position for medium to high
speed. At low speed, the rotor position is calculated using signal injection where a
high frequency low voltage signal is injected on the stator winding. In the proposed
method, the measured motor’s current and the estimated motor’s voltage are
processed through a signal processing block and a PI regulator to calculate the angle
of the rotor position.Finally the performance of the HMRASC and the rotor position angle estimation
algorithms are evaluated by simulation and verified experimentally for two motors
using MCK2407 kit and IMDM15 board which are based on the TMS320LF2407
fixed point Digital Signal Processor (DSP) for different operating conditions. The
first motor is rated at 50W and the second is rated at 380W. Both experimental and
simulation results obtained from the HMRASC and the position angle estimation
algorithms showed superior results compared to other methods presented in the
literature
Advancing Fast Frequency Response Ancillary Services in Renewable-Heavy Grids:A Global Review of Energy Storage-Based Solutions and Market Dynamics
No full textThis paper addresses the growing challenges and developments in frequency control within power systems influenced by the increasing penetration of renewable energy sources. It evaluates the advancements and limitations of renewable-based control technologies and explores the critical role of diverse energy storage technologies in providing fast frequency response ancillary services. Through a comprehensive analysis of the global literature, this paper categorises energy storage solutions according to their efficacy in meeting fast frequency response demands and potential for revenue generation. It reveals significant gaps in the current research, which predominantly focuses on battery energy storage systems and microgrid applications, with insufficient attention to grid-scale storage solutions and innovative energy storage technologies. This analysis identifies a lack of detailed technical simulations and hybrid storage models for frequency control, as well as a minimal exploration of the environmental benefits, particularly in terms of carbon dioxide emission reductions, associated with deploying new energy storage technologies in ancillary service markets. The paper concludes by emphasising the urgent need for further research incorporating detailed techno-economic evaluations and the carbon dioxide reduction potential of modular, scalable energy storage technologies, which should be facilitated by advanced network simulation models and comprehensive market analysis to drive future advancements in the field
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