Variable Load Profile Research Articles

A Modelica dynamic model of a supercritical CO2 energy conversion system for EU-DEMO

Nuclear fusion technology is one of the main valuable candidates for providing a trustable base load in low-carbon future energy scenarios, thanks to its power density, low emissions and flexibility, together with innovative power conversion systems that could improve its performance, as the supercritical CO2 Brayton cycle. Since the nuclear plant will be required to provide variable loads, due to the fluctuating nature of renewable energies, a powerful modelling tool will be necessary to simulate different power outputs. The present paper presents a dynamic model of a supercritical CO2 power cycle developed with Modelica, aimed at the design of control systems to adjust the power production according to the load required. Thanks to the activity of three PI controllers, the system is able to follow a variable load profile while preserving the turbomachinery inlet temperature, to avoid any possible damage of the devices. The work has shown the adequacy and potential interest of the use of Modelica in this kind of analyses.

Open-source Modelica models for the control performance simulation of chiller plants with water-side economizer

There are several cooling mode control sequences for chiller plants with water-side economizers adopted in industry and academia, and it is widely known that this supervisory control significantly affects energy consumption; however, there is a lack of a modeling resource to allow multiple control sequences to be evaluated systematically under different settings, such as system configurations, load types, and climate locations. To fill this gap, this paper develops open-source Modelica models to simulate the control and energy performance of multiple cooling mode control sequences for chiller plants with water-side economizers. These models allow users to develop and test their advanced control sequences for chiller plants with water-side economizers for their target climates and system configurations. To demonstrate how these models can be utilized, a chiller plant with an integrated water-side economizer is simulated using two advanced cooling mode control sequences, two cooling load types, and six climates, for a total of 24 simulation cases. This study revealed that the energy saving potential varied from 8.6% to 36.8% for constant load profiles in all of the considered climates, and from 6.3% to 25.8% for variable load profiles in most of the climates. Results also showed that the developed system models are able to capture transient control details and reveal counterintuitive energy performance.

INTEGRATING DC FAST/RAPID CHARGERS IN LOW VOLTAGE DISTRIBUTION NETWORKS

The continued dependence on fossil fuel-based vehicles with high carbon emissions for road transport has grown rapidly over the last years. In response to the pressing needs to achieve electrification of the transport sector to facilitate a reduction on greenhouse and other dangerous emissions, ultra-low carbon emission electric vehicles (EVs) have been proposed. However, it is important to ensure that existing infrastructure is designed to meet the large uptake of EVs and their charging technologies. The long charging battery durations have led the EV manufacturers to develop faster charging technology, known as DC fast charging, to increase the overall customer comfort (i.e. reduced charging duration). Fast chargers provide hundreds of kW of power and, therefore, the installed capacity of distribution networks will constrain the success of e-mobility. The aim of this paper is to analyse the impacts of integrating 250 kW DC fast chargers on a real low voltage distribution network in Cardiff, Wales. PSCAD/EMTDC is used to examine the operating conditions of the network under maximum and variable consumer load profiles. Simulation cases show that the 250 kW charger can be integrated without affecting the stability of the network while maintaining acceptable voltage operation.

Modeling of an isolated microgrid with hybrid PV-BESS system for peak load shaving simulation

In this paper, a simplified model of an isolated microgrid (IMG) with hybrid photovoltaic (PV)-battery energy storage system (BESS) is discussed. The concept of peak load shaving is also demonstrated. The developed model helps an isolated power system to minimize the demand and maximize the economic benefit. MATLAB/Simulink tool was used to simulate the electrical circuit of the IMG system. The IMG model contains four sub-systems, namely the PV model, time varying load profile model, BESS model and gas turbine generator (GTG) dispatch model. To evaluate the performance of the developed IMG, a simulation case study was conducted under realistic load conditions of Universiti Teknologi PETRONAS (UTP). Results indicated that simulation illustrates the precise situation of the real-time system, as found in the UTP microgrid. Results also revealed that the concept of peak load shaving broadly illustrated in this study. The implementation of the proposed IMG model may bring economic benefit by minimizing peak load demand. © 2021 Md Masud Rana et al.

Modeling and evaluation of nickel manganese cobalt based Li-ion storage for stationary applications

Lithium-ion (Li-ion) based batteries are commonly used in many applications such as electric vehicles, utility-scale storage, and consumer electronics. To maximize power utilization and optimize runtime voltage-current (V-I) characteristics, it is imperative to predict these batteries’ behavior for varying load profiles accurately. In this work, we first perform electrical modeling of two commonly used Li-ion cell chemistries, i.e., Nickel Manganese Cobalt (NMC) and Lithium Iron Phosphate (LiFePO4), using the modified Shepherd equation. Secondly, we evaluate the operation of NMC based Li-ion chemistry's operation for a stationary uninterruptible power supply (UPS) application commonly used in developing countries to cater to utility grid intermittency. The evaluation of cells is done through the 2RC model to ascertain the V-I behavior of these cells in a much more frequent charge-discharge regime in UPSs compared to standard EV applications. A UPS system's real charge/discharge pattern is taken with an average daily outage of up to 6 hrs. Finally, a comparison between the life and cost of Li-ion intervention is made and compared to a conventionally used lead-acid battery, which dominates over 90% of stationery UPS markets in developing countries.

An Echo State Network for fuel cell lifetime prediction under a dynamic micro-cogeneration load profile

Improving Proton Exchange Membrane Fuel Cell durability is a key that paves the way to its large scale industrial deployment. During the last five years, the prognostics discipline emerged as an interesting field for Proton Exchange Membrane Fuel Cell state of health prediction and lifetime estimation. The information provided by the prognostic module is crucial for optimizing the control strategy to extend the fuel cell lifetime. In this paper, an approach based on Echo State Network for fuel cell prognostics under a variable load is developed. The novelty of this paper is to perform prognostics under a variable load profile without prior knowledge of this latter. Two solutions are developed in this work. The first one consists of evaluating the remaining useful lifetime under a repeated load cycle. The second one is based on using Markov chains to generate estimations of the future load profile, allowing thus to overcome the need of real future load profile prior knowledge. Both proposed solutions give accurate prediction results of proton exchange membrane fuel cell remaining useful lifetime, with low uncertainties.

Distribution network reconfiguration based on artificial network reconfiguration for variable load profile

Network reconfiguration is a process to change the open-switches in distribution system for a minimum power loss. In the past, metaheuristic techniques were applied widely for network reconfiguration with consideration of a fixed loading profile. When the loading changes, the current configuration may not be the optimal one. Thus, the technique needs to be executed to find a new optimal configuration based on the latest loading. The process is time-consuming since metaheuristic techniques commonly require high computational times and produces inconsistent results. Therefore, this paper proposes a network reconfiguration technique based on artificial neural network (ANN) for variable loading conditions. The proposed ANN model is tested on IEEE 33-bus, IEEE 69-bus, and IEEE-118 bus systems. The test results indicate the efficiency of the proposed technique in three aspects: processing time, simple structure, and high accuracy.

Time-Series PV Hosting Capacity Assessment with Storage Deployment

Europe aims to diversify energy sources and reduce greenhouse gas emissions. On this field, large PV power growth is observed that may cause problems in existing networks. This paper examines the impact of distributed PV systems on voltage quality in a low voltage feeder in terms of the European standard EN 50160. As the standard defines allowable percentage of violation during one week period, time-series analyses are done to assess PV hosting capacity. The simulations are conducted with 10-minute step and comprise variable load profiles based on Gaussian Mixture Model and PV profiles based on a distribution with experimentally obtained parameters. In addition, the outcomes are compared with “snapshot” simulations. Next, it is examined how energy storage utilization affects the hosting capacity. Several deployments of energy storages are presented with different number and capacity. In particular, a greedy algorithm is proposed to determine the sub-optimal energy storage deployment based on the voltage deviation minimization. The simulations show that time-series analyses in comparison with snapshot analyses give completely different results and change the level of PV hosting capacity. Moreover, incorrect energy storage capacity selection and location may cause even deterioration of power quality in electrical systems with high RES penetration.

Reliability of Steel Truss Roof Systems Under Variable Snow Load Profiles

The probabilistic safety analysis of steel truss roof systems under variable snow load profiles is investigated. The roofs of structures such as industrial buildings or sports halls, which require wide areas, are frequently subjected to unexpectedly high loads. Therefore, compared to the residential buildings, that type of buildings often comes across failure or prohibition of usage. Probabilistic techniques are utilized for the analysis of the problem. Thirty-six steel roof structures with different structural dimensions and load variations are modelled and their failure probabilities are calculated. In this paper, a complicated stochastic analysis is reduced to the solution of a load–resistance (S–R) problem by utilizing sensitivity analyses. Firstly, the structures were designed through structural analyses, and then the sensitivity analyses were conducted to understand the response of the structure to the load and to the span-length parameters. In this paper, a reliability study which gradually monitors the effect of geometrical parameters on the failure trend is presented. Three different snow load distribution functions were used. The relation between failure, and load or structural dimension variations was investigated. The results obtained in this study are discussed and compared with the results from the literature for similar structures subjected to snow loads. It is observed that for the snow load distributions with high standard deviations the structural reliability indices may give results below the target safety levels of the design codes. Finally, the assessment of the results shows that the effect of the standard deviation of the snow load on failure probability is much more than the effect of intensity of the nominal snow load.

Stationary gas turbines: an exergetic approach to part load operation

As renewables are progressively displacing thermal plants in the power generation scene worldwide, the vocation of stationary Gas Turbines (GT) is deeply evolving. In this irreversible move GT plants are called upon to become cycling units with increasingly variable load profiles. This is dictated by the need to compensate for the fluctuations of renewable energy sources and secure the spinning reserve that is indispensable for the stability of the grids. This new scenario creates a serious challenge for gas turbine designers and operators in terms of investment policy, plant management and equipment lifetime. Indeed, operating a gas turbine at part, variable load requires reducing its firing temperature and possibly its air flow. While part load operation entails efficiency losses with respect to the full load mode, load variations cause maintenance penalties due the premature component ageing tied namely with thermal and low cycle fatigue effects on machine materials. As far as efficiency is concerned, an exergy analysis of a contemporary, air-based Brayton cycle is useful for quantifying and comparing the losses incurred by the various engine components. Such study reveals the high sensitivity of compressor efficiency to load decreases. Among possible counter-measures, heating the air at the compressor intake represents a simple mitigation measure, as it enables reducing the air flow rate while preserving to some extent the efficiency of the compressor and consequently GT efficiency.

Neural Network Integrated Adaptive Backstepping Control of DC-DC Boost Converter

This paper deals with the output voltage regulation problem of dc-dc boost converter feeding a resistive load. A new control mechanism based on Chebyshev neural network embedded in an adaptive backstepping framework is proposed for the boost converter control. Since the converter is complex, time varying and non-linear in nature, it exhibits high sensitivity to unanticipated disturbances in the load current. Hence, designing a robust control mechanism to attain a satisfactory transient and steady state performance over a wide range of operating points is a challenging task. In this work, a control law is derived based on the systematic and recursive design strategy of adaptive backstepping method. A single layer functional link Chebyshev neural network is employed for a fast estimation of uncertain and time varying load profile of the boost converter. The stability of overall converter equipped with the proposed controller is proved using Lyapunov stability criterion. Further, in order to validate the proposed methodology, the boost converter is simulated in MATLAB/Simulink software and is subjected to different load perturbations. The efficacy of the proposed control is highlighted by evaluating it against the conventional adaptive backstepping control under identical conditions. The results obtained reveals that the proposed control is much faster in estimating the unknown load parameter and offers satisfactory output voltage tracking, yielding fast response and low peak overshoot/undershoot in the event of unknown load perturbations. Experimental investigation using dspace DS1103 controller is further carried out to validate the efficacy of proposed control scheme.

Techno Economic Evaluation of a Hybrid Energy System

Use of renewable electricity resources has proved to be a more reliable and cleaner solution now-a-days. Photovoltaic units, wind turbine units have been considered as these are the most commonly used renewable energy generators today, but these generators involve high values of losses and are highly expensive. This paper presents a multi objective optimization using the Matlab GAtool. The algorithm is executed on a test system of 69 buses. The results of the case study show that proper positioning of renewable energy generators and sizing can lead to improvement in voltage profile, reduction in power losses and reduction in cost. Cost analysis of this framework is performed using Homer Pro3.8.1 which shows that this hybrid energy system is more efficient and economical than using of a single renewable electricity generator or combinations of renewable electricity generators of different sizes for a variable load profile. The advantages of this algorithm include its accuracy, calculation speed and the reduced values.

A bearing fault model for Independent Cart Conveyor System and its validation

Independent Cart Conveyor System is one of the most promising technology in automation industries. It combines the benefits of servo motors with the advantages of linear motors. It consists of a close path made up of modular linear motors having a curved or a straight shape that control a fleet of carts independently. Each cart is placed along the motors and it is connected, through rolling bearings, to a rail set on the motors themselves. The bearings are subject to wear and the condition monitoring of these elements is challenging for the non-stationary working conditions of variable load and variable speed profiles. This paper provides a bearing fault vibration model that takes into account the mechanical design of the cart, its motion profile, the shape of the conveyor path, the load variation and the type of fault on the rolling bearing.

Quantification of aging mechanisms and inhomogeneity in cycled lithium-ion cells by differential voltage analysis

Understanding the aging behavior in lithium-ion cells for automotive applications and its underlying physical-chemical degradation processes that lead to a decrease in energy and power density is crucial for the prediction of battery lifetime. Furthermore, the process of parameterization for physical-chemical motivated models with implemented aging mechanisms is a challenging task. In this work, we present a technique to quantify the loss of cyclable lithium and the decrease of electrode capacities. In addition, the inhomogeneous distribution of lithium inside the cell is estimated by introducing a fit factor to describe the vanishing of peaks in the first derivative of the open circuit voltage curve in respect to state of charge. Differential voltage analysis is performed using an algorithm inspired by pattern search. The algorithm uses the idea of superpositioning multiple open circuit voltage curves with individual distribution of losses. A dataset of ten automotive pouch cells with NMC-622 cathode and graphite anode that were cycled with varying load profiles is used for testing and validation of the method. The method reveals significant differences in the aging behavior and state of homogeneity of cells that cannot be obtained by solely analyzing capacity and resistance evolution.

Optimal allocation of distributed generation for planning master–slave controlled microgrids

This study proposes a novel problem formulation for a planning distributed generation (DG) allocation for microgrids, using the master–slave approach. In the previous planning studies, all DGs have the same operating mode (e.g. operate at unity power factor). For master–slave controlled microgrid, DGs have two possible operating modes: master (non-unity power factor operation) and slave (unity power factor operation). For planning a master–slave controlled microgrid, in addition to DG siting, the optimal DG operating mode is determined by including a new set of constraints in the planning problem. Thus, the proposed formulation is capable of determining the optimal location of the master and slave DGs with the main objective of minimizing the microgrid's energy losses. The proposed model is formulated as a mixed-integer non-linear programming problem; incorporated into an optimal power flow framework and tested on the IEEE 38-bus systems considering a variable load profile. In addition to this, sensitivity analysis is carried for case studies with different load types and reactive power injection by the slave DGs in the system (e.g. operate at fixed non-unity power factor). The proposed approach can serve as an efficient tool for utility operators for planning microgrids.

2 kW Modular PEM fuel cell stack for space applications: Development and test for operation under relevant conditions

A polymer electrolyte fuel cell stack of 2 kW of peak power has been designed, manufactured and tested. Such a fuel cell stack has been envisaged to be applied for manned lunar exploration missions, as secondary energy source for both mobile vehicles and fixed power plants. The stack has been bread-boarded in order to test and demonstrate the technology. The following constraints have been considered during the study: (i) operation with pure hydrogen and oxygen (ii) low humidification levels (iii) use of commercial Membrane Electrode Assemblies. The breadboard has been made consisting of two modules, each of 500 W of nominal power. Specific tests have been conducted to prove the effect of gravity on the breadboard performance and verify its ability to follow variable load profiles over time, representative of different lunar surface exploration missions. The bread-board has shown a good performance during the test campaign, even if some failures of the Membrane Electrode Assemblies have been observed. The study has demonstrated the high performance of the developed stack and the potential of fuel cell technology for use in future human lunar exploration missions. At the same time, it has highlighted serious criticalities related to the reliability and robustness of the commercially available Membrane Electrode Assemblies, which need to be investigated further, before the use of this technology in a real space exploration mission can be possible.

The Influence of Current Ripples on the Lifetime of Lithium-Ion Batteries

In electric vehicles (EVs) and other applications, lithium-ion batteries experience variable load profiles with frequencies up to several kilohertz, as caused by power electronics. It is crucial to know if certain frequencies accelerate battery degradation and should be avoided. This applies in particular for EV batteries with an expected lifetime of more than ten years. This study investigates the influence of alternating current (ac) profiles on the lifetime of lithium-ion batteries. High-energy battery cells were tested for more than 1500 equivalent full cycles to practically check the influence of current ripples. The applied load profiles consisted of a constant current with superimposed ac frequencies. The frequencies were chosen with regards to the impedance spectra and ranged from 30 kHz to 1 Hz. The measurement results are compared to published results and to a second series of experiments conducted with high-power battery cells. The results of the experiments indicate that lithium-ion battery cells cycled with low frequencies experience a 1% to 2% higher impedance increase and capacity fade than battery cells cycled with high ac frequencies. These differences in aging characteristics established within the first 50 cycles of the brand-new battery cells and remained almost constant thereafter. For these experiments, the antithesis is disproved since no accelerated degradation occurred at higher frequencies. Therefore, with regards to battery lifetime, high frequencies can be tolerated as long as temperatures are considered as well. This new finding may help us to reduce the costs of products with complex battery systems, such as EVs.

Unified Probabilistic Modeling of Wind Reserves for Demand Response and Frequency Regulation in Islanded Microgrids

Islanded microgrids provide unique challenges due to the lack of transmission support, requiring local energy supply and grid regulation architecture. This paper presents a unified probabilistic assessment of wind reserves for demand response (DR) and frequency regulation in islanded microgrids. A multivariate nonparametric kernel density estimation algorithm is used to generate the probabilistic models of the wind resource, electrical demand, and predicted performance of wind generation. These models are numerically combined to evaluate the capability of wind generation to act as a dynamic reserve and predict its performance for DR, secondary generation, and frequency regulation in an islanded system. The probabilistic model captures multivariate cross-correlation, nonstationary environmental and load behavior, as well as multimodality in their underlying probability distributions. A case study is conducted to validate the proposed model, which predicts wind generation effectiveness for varying load profiles, wind profiles, and generation capacities. PLEXIM simulation software is used to implement a model microgrid to demonstrate the integration of wind generation and its regulatory capabilities. The proposed algorithm has applications in power system planning and operation, and it provides a method using probabilistic data set for long term energy management and optimization of islanded microgrids.

New Strategy Based on Combined Use of Genetic Algorithm and Gradient to Solve the UC Problem: Theoretical Investigation and Comparative Study

This paper presents a comparative study between a new strategy based on hybrid Gradient-Genetic Algorithm method and metaheuristic methods for solving Unit Commitment problem. Strategies have been applied on the IEEE electrical network 14 bus test system for a variable load profile during a discretized margin of time (24-hour time requirement). The right choice of the initial population and the best knowledge of the technical constraints specific to each generator (power balance constraints, Spinning reserve constraints, minimum up time, minimum down time ) suggests the possibility of obtaining improvements in the time execution. The adopted strategy has presented high performance both for minimizing the production cost and for the rapidity of convergence to optimal solutions and is promising compared to Genetic algorithm.

Variable load control strategy for room-temperature magnetocaloric cooling applications

Room temperature magnetocaloric cooling is more environmental friendly but not yet more energy efficient than the state-of-the-art vapor compression technology. In this paper, we provide one more argument to support magnetic cooling technology, which is the superior energy saving potential under part load conditions. Therefore, magnetic cooling system may not compete with vapor compression under full load nominal condition, but its seasonal or annual overall efficiency could be better when part load characteristics are taken into account. To show the operation feasibility under part load condition, a feedback control strategy is proposed and incorporated into a magnetic cooling system model in Simulink first. The robust control quality is then revealed by numerical simulation studies for five different variable part load profiles. Furthermore, the transient accumulated energy performances are compared with those estimated based on the quasi-steady state condition to simplify the calculation on the overall energy efficiency benefit. Finally, a case study is carried out for unitary air-conditioning application, revealing that the overall energy efficiency is almost twice of the energy efficiency evaluated under full load condition.