Load Flow Research Articles

Dynamic kinetic studies of lysozyme removal as protein waste using weak ion exchange nanofiber membranes in flow systems: Linear and nonlinear model analysis

BackgroundThis study explores lysozyme removal using a weak ion exchange nanofiber membrane in a continuous flow system. Understanding removal kinetics is critical for designing, optimizing, and scaling industrial processes. Kinetic models play a crucial role in predicting removal behaviors, including the pseudo-first-order, pseudo-second-order, Elovich, Avrami, and intra-particle diffusion models. Each model provides different insights based on its assumptions, making them suitable for analyzing various removal systems and mechanisms. MethodsThe study investigated the effects of four parameters—removal pH, initial lysozyme concentration, loading flow rate, and the number of membranes stacking layers—on dynamic kinetic binding behavior. Both linear and non-linear kinetic models were employed to analyze experimental results, focusing on removal rates and mechanisms. Significant FindingsAnalysis revealed key insights into removal kinetics. The change in removal pH significantly affected the binding rate and capacity. Higher initial concentrations increased binding rates, while changes in loading flow rate influenced removal rate and capacity. Increasing the number of membrane stacking layers enhanced the removal capacity and increased the pressure drop. These findings underscore the importance of optimizing parameters for efficient removal in biotechnology and wastewater treatment.

Enrichment of polyphenols from Cinnamomum camphora seed kernel by macroporous adsorption resins and evaluation of its antioxidant and enzyme inhibitory activities

The Cinnamomum camphora seed kernel (CCSK) has potential as a natural source of bioactive phenolics. However, there is limited information on the recovery and characteristics of polyphenols from the by-product of CCSK after oil extraction. In this study, an efficient macroporous adsorption resin technology was established to enrich polyphenols from CCSK (CCSK-P). HJ-18 resin was found as the most appropriate adsorbent because of its highest adsorption/desorption capacity for CCSK-P. The pseudo-second-order kinetic and Freundlich isotherm models better described the whole adsorption process. The adsorption process was spontaneous, physical, and dominated by multilinear intraparticle diffusion. The optimum enrichment procedure of CCSK-P on HJ-18 resin was as follows: for adsorption, loading flow rate and sample volume were 1.5 BV/h, and 4.79 BV, respectively; for desorption, the eluent type, elution flow rate and volume were 80% ethanol, 3.0 BV/h and 8 BV, respectively. After one-step large-scale enrichment, the total polyphenol content of CCSK-P was increased 7.11-fold with a recovery of 81.07%. Moreover, enriched CCSK-P showed significantly higher scavenging ability against DPPH radical and ABTS radical and higher inhibitory activity against α-amylase and α-glucosidase. In conclusion, CCSK-P was well enriched by HJ-18 resin and exhibited good antioxidant and enzyme inhibition activity, which may be applied in the food, cosmetic and pharmacy industries. This study provides a meaningful guidance for the industrial enrichment of polyphenols from CCSK.

Stochastic Scheduling of Grid-Connected Smart Energy Hubs Participating in the Day-Ahead Energy, Reactive Power and Reserve Markets

An Energy Hub (EH) is able to manage several types of energy at the same time by aggregating resources, storage devices, and responsive loads. Therefore, it is expected that energy efficiency is high. Hence, the optimal operation for smart EHs in energy (gas, electrical, and thermal) networks is discussed in this study based on their contribution to reactive power, the energy market, and day-ahead reservations. This scheme is presented in a smart bi-level optimization. In the upper level, the equations of linearized optimal power flow are used to minimize energy losses in the presented energy networks. The lower level considers the maximization of profits of smart EHs in the mentioned markets; it is based on the EH operational model of resource, responsive load, and storage devices, as well as the formulation of the reserve and flexible constraints. This paper uses the “Karush–Kuhn–Tucker” method for single-level model extraction. An “unscented transformation technique” is then applied in order to model the uncertainties associated with energy price, renewable energy, load, and energy consumed in mobile storage. The participation of hubs in the mentioned markets to improve their economic status and the technical status of the networks, modeling of the flexibility of the hubs, and using the unscented transformation method to model uncertainties are the innovations of this article. Finally, the extracted numerical results indicate the proposed model’s potential to improve EHs’ economic and flexibility status and the energy network’s performance compared to their load flow studies. As a result, energy loss, voltage, and temperature drop as operation indices are improved by 14.5%, 48.2%, and 46.2% compared to the load flow studies, in the case of 100% EH flexibility and their optimal economic situation extraction.

Eco-power management system with operation and voltage security objectives of distribution system operator considering networked virtual power plants with electric vehicles parking lot and price-based demand response

In the energy management of a network, it is expected that by extracting the optimal performance for the power sources, storage equipment, and responsive demand, a favorable economic and technology situation is achievable for the network and the mentioned elements. Virtual power plants, as a unit aggregating resources, storage, and responsive loads, can create more favorable conditions in network energy management. So, it is expected that the positive effect of the virtual power plant format on the economic and technical situation of the distribution system is far more than those of managing individual elements mentioned in the network. Consequently, the distribution network operator's economic, environmental, and technical goals are met through the concurrent administration of reactive and active power in the smart distribution network that is equipped with a flexible-sustainable virtual power plant. The system operator is accountable for reducing the weighted sum of the voltage security index, energy loss, and energy cost of the distribution network. This problem is associated with the optimal power flow formulation, which considers the environmental limits and security of voltage in the distribution network, the renewable resource operation model and flexibility in the form of a virtual power plant, and the system's flexibility constraints. Flexibility resources considered in the present study are price-based demand response and electric vehicle parking lots. Stochastic optimization relying on the Unscented Transform assists in providing a suitable model for uncertain quantities resulting from the amount of load, electric vehicles, renewable power, and price of energy and eventually shortens the computing time and accurately computes the flexibility index. The optimal compromise solution amongst various objective functions can be found through fuzzy decision-making. Some innovations of this research include concurrent administration of active and reactive power in virtual power plant, concurrent modeling of economic, operational, environmental, voltage security, and flexibility indicators in the distribution network, utilization of electric vehicles, and demand response as a source of flexibility, use of Unscented transform for modeling the uncertainties corresponding to the exact calculation of flexibility. The suggested method was simulated in the IEEE 69-bus radial smart distribution system. Regarding the numerical report obtained, the optimal performance of each of the renewable generation, demand response, and parking of electric vehicles can significantly impact the economic and technical condition of the distribution network. However, the best condition was obtained when the mentioned elements were placed in the form of a virtual power plant. So, in such a situation, the energy cost is around $1862 for the said network. The lowest value for the worst security index in this network is around 0.933 p.u. Energy loss, maximum voltage drop, and peak load carrying capability are equal to 1.902 MWh, 0.047 p.u., and 5.624 MW, respectively. As a result, and based on numerical findings, the method can attain sustainable social welfare. The optimal power scheduling of sustainable systems can enhance the economic, security of voltage, and operational, conditions of the network by roughly 43 %, 26.9 %, and 47 %-62 %, respectively, compared to power flow studies. Furthermore, the ideal administration of virtual power plants enables the proposed plan to achieve 100 % flexibility. Additionally, it can substantially diminish the degree of contamination within the distribution network.

Impact of Electric Vehicles Charging on Urban Residential Power Distribution Networks

Achieving transportation decarbonization and reducing carbon emissions are global initiatives that have attracted a lot of effort. The use of electric vehicles (EVs) has experienced a significant increase lately, which will have a considerable impact on current power systems. This study develops a framework to evaluate/mitigate the negative impact of increasing EV charging on urban power distribution systems. This framework includes data analytics of actual residential electrical load and EV charging profiles, and the development of optimal EV charging management and AC load flow models using an actual residential power distribution system in Saskatchewan, Canada. We use statistical methods to identify a statistically-extreme situation for a power system, which a power utility needs to prepare for. The philosophy is that if the power system can accommodate this situation, the power system will be stable 97.7% of the time. Simulation results show the house voltage and transformer loading at various EV penetration levels under this statistically-extreme situation. We also identify that the particular 22-house power distribution system can accommodate a maximum number of 11 EVs (representing 50% EV penetration) under this statistically-extreme situation. The results also show that the proposed optimal EV charging management model can reduce the peak demand by 43%. Since we use actual data for this study, it reflects the current real-world situation, which presents a useful reference for power utilities. The framework can also be used to evaluate/mitigate the impact of EV charging on power systems and optimize EV infrastructure development.

Streamlined Clarification and Capture Process for Monoclonal Antibodies Using Fluidized Bed Centrifugation and Multi-Column Chromatography With Membrane Adsorbers.

Harmonizing unit operations in the downstream process of monoclonal antibodies (mAbs) has a high potential to overcome throughput limitations and reduce manufacturing costs. This study proposes a streamlined clarification and capture (S-CC) process concept for the continuous processing of cell broth harvested from a connected bioreactor. The process was realized with a fluidized bed centrifuge connected to depth and sterile filters, a surge tank, and a multi-column chromatography (MCC) unit. The MCC unit was operated in the rapid cycling simulated moving bed (RC-BioSMB) mode with five convective diffusive membrane adsorbers (MAs). A control strategy and the surge tank were used to adjust the loading flow rate of the MCC unit. The mAb was recovered with a total process yield of 90%, with high removal of the process-related impurities HCP (2.1 LRV) and DNA (2.9 LRV). Moreover, the S-CC process productivity of 4.2 g h- 1 was up to 5.3 times higher than for comparable, hypothetical batch MA processes. In addition, the buffer consumption of the capture step could be reduced from 2.0 L g- 1 in batch mode to 1.2 L g- 1 in the RC-BioSMB mode. These results demonstrate the high potential of streamlined interconnected unit operations to improve the overall mAb downstream process performance.

Stochastic economic sizing and placement of renewable integrated energy system with combined hydrogen and power technology in the active distribution network

The current study concentrates on the planning (sitting and sizing) of a renewable integrated energy system that incorporates power-to-hydrogen (P2H) and hydrogen-to-power (H2P) technologies within an active distribution network. This is expressed in the form of an optimization model, in which the objective function is to reduce the annual costs of construction and maintenance of integrated energy systems. The model takes into account the planning and operation model of wind, solar, and bio-waste resources, as well as hydrogen storage (a combination of P2H, H2P, and hydrogen tank), and the optimal power flow constraints of the distribution network. Electrical and hydrogen energy are administered in an integrated energy system. The modeling of the uncertainties regarding the quantity of load and renewable resources is achieved through stochastic optimization using the Unscented Transformation method. The novelties of the scheme include the sizing and placement of a combined hydrogen and power-based renewable integrated energy system, the consideration of the impacts of bio-waste units, P2H, and H2P systems on the planning of the integrated energy system and the operation of the active distribution network, and the modeling of uncertainties using the Unscented Transformation method to reduce the calculation time. The study’s results demonstrate the scheme’s ability to improve the technical conditions of the distribution network by considering the optimal planning of integrated energy systems. In comparison to the network power flow, the operation status of the network has been improved by approximately 23-45% through the optimal siting, sizing, and energy management of hydrogen storage equipment, as well as renewable resources in the form of integrated energy systems. In other words, optimal energy management and planning of the integrated energy systems in the distribution network has been able to reduce energy losses and voltage drop by 44.5% and 42.4% compared to the load flow studies. In this situation, peak load carrying capability has increased by about 23.7%. In addition, compared to the case of the network with renewable resources, the overvoltage has decreased by about 43.5%. Also, Unscented Transformation method has a lower calculation time than scenario-based stochastic optimization.

Static Security Assessment: A Case Study of the Saudi National Grid

The static security assessment of the Saudi National Network helps to identify the weaknesses and potential threats and provides a comprehensive understanding of the network’s ability to address such incidents due to probable outages. This paper presents the static security assessment of the Saudi national grid with reference to the transmission line overloads and the bus voltage deviations in the system following the initiation of the specified contingencies. Critical contingencies are identified, and these are ranked on the basis of the limit violations of the line overloads and the bus voltage deviations obtained through the Newton-Raphson load flow analysis. The system loading conditions considered correspond to that of a typical day of the Hajj Pilgrimage period, during which millions of pilgrims from various parts of the country and also the world visit the Holy Mosque at Makkah. The preliminary investigations reveal that there are some transmission lines and certain substations requiring special attention to enhance the system security.

Multi-Objective Optimization Algorithm Based Bidirectional Long Short Term Memory Network Model for Optimum Sizing of Distributed Generators and Shunt Capacitors for Distribution Systems

In this paper, a multi-objective grey wolf optimization (GWO) algorithm based Bidirectional Long Short Term Memory (BiLSTM) network machine learning (ML) model is proposed for finding the optimum sizing of distributed generators (DGs) and shunt capacitors (SHCs) to enhance the performance of distribution systems at any desired load factor. The stochastic traits of evolutionary computing methods necessitate running the algorithm repeatedly to confirm the global optimum. In order to save utility engineers time and effort, this study introduces a BiLSTM network-based machine learning model to directly estimate the optimal values of DGs and SHCs, rather than relying on load flow estimates. At first, a multi-objective grey wolf optimizer determines the most suitable locations and capacities of DGs and SHCs at the unity load factor and the same locations are used to obtain optimum sizing of DGs and SHCs at other load factors also. The base case data sets consisting of substation apparent power, real power load, reactive power load, real power loss, reactive power loss and minimum node voltage at various load factors in per unit values are taken as input training data for the machine learning model. The optimal sizes of the DGs and SHCs for the corresponding load factors obtained using GWO algorithm are taken as target data sets in per unit values for the machine learning model. An adaptive moment estimation (adam) optimization approach is employed to train the BiLSTM ML model for identifying the ideal values of distributed generations and shunt capacitors at different load factors. The efficacy of the proposed ML-based sizing algorithm is demonstrated via simulation studies.

Three‐phase four‐wire power flow solution for multi‐grounded distribution networks with non‐bolted grounding

AbstractThis study proposes a direct three‐phase four‐wire power flow method to accurately analyse the neutral line and multiple grounding characteristics. In particular, the proposed grounding impedance building‐based solution method was used to analyse the neutral grounding impedance in power flow studies based on the slack bus grounding impedance. The accuracy of the proposed method was verified using a neutral‐to‐earth voltage test system. IEEE 13‐bus and 123‐bus test systems were used to compare the advantages and disadvantages of the proposed method. Compared to the current injection full Newton and forward–backward sweep methods, the proposed method achieves a significant reduction in iteration numbers of up to 76.92% and 77.78%, respectively. For different grounding scenarios, stable convergence characteristics were exhibited by the proposed method after six to seven iterations.

Optimal Planning of Standalone Rural Microgrid With Effective Dispatch Strategies and Battery Technology

ABSTRACTMicrogrids are a viable substitute for traditional power systems because they may deliver cleaner, more dependable, affordable power with fewer losses. However, the microgrid's performance is impacted by the variable nature of renewable energy sources. Battery storage is a crucial component of microgrid planning since it defines the system's techno‐economic feasibility. A standalone rural microgrid is designed in the current study, employing three distinct battery types: lithium‐ion, lead acid, and zinc‐bromine flow. The suggested microgrid's techno‐economic analysis employs three distinct dispatch mechanisms, that is, cycle charging, load flow, and complete dispatch. The case study of the suggested framework is carried out in Lucknow (India). The system comprises PV/battery/wind energy system/diesel and battery. The simulation results suggest that the optimal system with the least electrification cost is 0.113 $/kWh using complete dispatch strategies and a zinc‐bromine battery. It has 206 kWh zinc‐bromine flow batteries, a 10 kW converter, a 20 kW PV, a 13‐kW diesel generator, and a Combined dispatch strategy. The system's net present cost per unit cost of energy is $40 275 and 0.113 $/kWh for the chosen region, respectively. Compared to the other two battery technologies for hybrid systems, the zinc‐bromine flow battery technology is also shown to be the most environmentally friendly. Among the three battery technologies available, zinc‐bromine flow is best used in a particular location's hybridized operation.

Optimal Location Identification of Solar PV Systems in Distributed Generators Based on Prediction of Load Flow and Factor Using Rule Based Deep Learning Algorithm

Optimal Location Identification of Solar PV Systems in Distributed Generators Based on Prediction of Load Flow and Factor Using Rule Based Deep Learning Algorithm

A new analytical technique for obtaining the optimal sizing, location, and scheduling of BESS in a grid-connected microgrid with multiple cases of heavy DG penetration

This paper aims to provide an optimal location, power, and energy rating for a battery energy storage system (BESS) in a grid-connected microgrid. The microgrid is pre-installed with heavy renewable distributed generations like solar and wind power plants. The paper suggests a straightforward optimal operating schedule for BESS, considering real-time data on solar irradiance, wind speed, load profile, and electricity tariff obtained from the US Energy Information Administration. The proposed method analytically identifies the optimal size and location of the storage system using the modified Q-PQV load flow technique. The method also proposes incorporating seasonal variations of the real-time data to obtain the optimal BESS size. A detailed cost-benefit analysis is exhibited to validate the economic feasibility. The methodological superiority is established by comparing the proposed algorithm with other soft computing techniques like PSO, GA, and JAYA algorithms. The novel technique is executed on 33-bus and 136-bus test systems with multiple cases of solar and wind-distributed generations. The proposed method provided a significant reduction in annual energy loss with a definite improvement in the voltage profile and overall profit of the system.

Renewable energy integration impact on power quality of supply of transmission system

Electrical energy is known as an essential part of our day-to-day lives. Renewable energy resources can be regenerated through the natural method within a reasonably short time and can be used to bridge the gap in extended power outages. Achieving more renewable energy (RE) than the low levels typically found in today’s energy supply network will entail continuous additional integration efforts into the future. This study examined the impacts of integrating renewable energy on the power quality of transmission networks. This work considered majorly two prominent renewable technologies (solar photovoltaic and wind energy). To examine the effects, IEEE 9-bus (a transmission network) was used. The transmission network and renewable sources (solar photovoltaic and wind energy technologies) were modelled with MATLAB/SIMULINK®. The Newton-Raphson iteration method of solution was employed for the solution of the load flow owing to its fast convergence and simplicity. The effects of its integration on the quality of the power supply, especially the voltage profile and harmonic content, were determined. It was discovered that the optimal location, where the voltage profile is improved and harmonic distortion is minimal, was at Bus 8 for the wind energy and then Bus 5 for the solar photovoltaic source.

Development of membrane distillation powered by engine exhaust for water desalination

To address water shortage in arid areas and make use of waste heat, an experimental investigation is presented for a novel system of air gap membrane distillation unit driven by an engine exhaust heat source for the development of an on-board car water desalination. The design of the gas-to-water heat exchanger is presented and optimized. The system’s performance, governed by the achieved feed water temperature and measured by the output vapor mass flux, is evaluated with time under varying operational parameters, including engine load, engine speed, and feed water flow rate, with the analysis of production cost for economic viability. Experimental results revealed that higher engine loads and speeds lead to rapid temperature increase of the feed water, enhancing the efficiency of the membrane distillation process and increasing the permeate flux. Notably, a maximum permeate flux of 45 kg/m2h was achieved after 40 minutes of operation. A minimum production cost of 3.2 $/m3 is estimated at an engine load of 35 N.m. With production costs ranging from 3.2 to 5.8 $/m3, the proposed system emerges as a highly competitive option when compared with other membrane distillation systems driven by waste heat in literature. This study underscores the potential of utilizing waste heat from car engines for cost-effective and sustainable water desalination, paving the way for further development and implementation of the system as a mobile desalination unit for different applications.

AI and Machine Learning in V2G technology: A review of bi-directional converters, charging systems, and control strategies for smart grid integration

Electric Vehicles (EVs) are transforming the transportation sector, and their integration with the grid is crucial for a sustainable energy future. EVs can serve as distributed energy resources, aiding in peak shaving, frequency management, and voltage support, thus enhancing grid stability. This comprehensive review explores the transformative potential of EVs in the power grid, focusing on Vehicle-to-Grid (V2 G) technology. We discuss different bidirectional Converter types, including AC-DC and DC-DC converters, to optimize power flow and voltage regulation. AC-DC converters rectify AC grid power for DC charging, while DC-DC converters optimize DC power flow and voltage regulation. Charging station safety is paramount, with electrical shock protection, fire protection, and cybersecurity measures essential for ensuring safe and reliable charging. The review also delves into energy trading and security in blockchain management, highlighting the use of blockchain technology to address hacking vulnerabilities. We explore the potential of Artificial Intelligence (AI) and Machine Learning (ML) algorithms to optimize V2 G performance. By leveraging AI and ML, we can improve the efficiency, reliability, and scalability of V2 G systems. AI-powered predictive analytics can forecast energy demand and supply, enabling proactive charging and discharging strategies. ML algorithms can optimize charging rates, battery health, and grid stability while also detecting anomalies and preventing potential faults. By integrating AI and ML into V2 G systems, we can unlock new possibilities for sustainable energy management, grid resilience, and electric vehicle adoption.

A Steady State Model of Solid-State Transformer for a Sequential AC–DC Power Flow in AC–DC Power Grids

A Steady State Model of Solid-State Transformer for a Sequential AC–DC Power Flow in AC–DC Power Grids

Simultaneous Impacts of Correlated Photovoltaic Systems and Fast Electric Vehicle Charging Stations on the Operation of Active Distribution Grids

This paper presents two novel probabilistic models developed to account for the uncertainties of aggregated fast electric vehicle charging stations (FEVCSs) demand and correlated photovoltaic (PV) injections in active distribution network (ADN) analysis. Both models are more precise than the available ones. A probabilistic model based on the Beta distribution is used for solar radiance, while the shared random variables technique is proposed considering correlated solar radiation random variables. Furthermore, a probabilistic negative exponential load model is extended for modeling the FEVCSs based on the Weibull probability density function. Moreover, the proposed probabilistic load flow (PLF) model is solved using the combined cumulants and saddle-point approximation method. Numerical tests are provided and discussed by applying the IEEE 69-bus distribution system for different PV correlation coefficients and FEVCS load models. The results demonstrate how the uncertainty of PLF outputs is increased by integrating FEVCSs and correlated PV resources into the distribution network. In addition, simulation results validate that the cumulants-based methodology provides satisfactory accuracy with a low computational cost.

A Review of Reliability Research in Regional Integrated Energy System: Indicator, Modeling, and Assessment Methods

The increasing complexity of integrated energy systems has made reliability assessment a critical challenge. This paper presents a comprehensive review of reliability assessment in Regional Integrated Energy Systems (RIES), focusing on key aspects such as reliability indicators, modeling approaches, and evaluation techniques. This study highlights the role of renewable energy sources and examines the coupling relationships within RIES. Energy hub models and complex network theory are identified as significant in RIES modeling, while probabilistic load flow analysis shows promise in handling renewable energy uncertainties. This paper also explores the potential of machine learning methods and multi-objective optimization approaches in enhancing system reliability. By proposing an integrated assessment framework, this study addresses this research gap in reliability evaluation under high renewable energy penetration scenarios. The findings contribute to the advancement of reliability assessment methodologies for integrated energy systems, supporting the development of more resilient and sustainable energy infrastructures.

Modeling of hybrid renewable resources and comprehensive feasibility analysis of grid interactive energy system for commercial building: a case study

ABSTRACT The growing energy demand in commercial buildings and businesses poses significant environmental challenges due to the dependence on fossil fuel-based energy. To address this issue, increasing the use of renewable energy sources and decreasing reliance on fossil fuels is crucial. Commercial buildings, in particular, have many opportunities to harness energy from renewable resources, such as solar, wind, and biomass. This study analyzed historical energy consumption data from a commercial building using MATLAB to forecast future energy demands. A range of machine learning algorithms were employed to accurately predict energy consumption, including Ensemble Boosting, Ensemble Bagging, Decision Trees, Support Vector Machines (SVM), and Neural Networks (NN). A Hybrid Renewable Energy (HRE) system was designed to meet the building’s energy needs, integrating renewable sources with grid-interactive inverters. Performance metrics for the forecasting models were calculated and documented, followed by a detailed techno-economic and environmental feasibility analysis of the HRE system. Different levels of renewable energy penetration were explored to optimize HRE designs using load-following dispatch algorithms tailored to the commercial building’s specific needs. The study revealed that achieving a renewable energy proportion of 12.6% resulted in the lowest cost of electricity (COE) at $0.135 and a Net Present Cost (NPC) of $8.84 million. Additionally, a reliability study using load flow techniques confirmed the stability of the optimized Hybrid Renewable Energy system. The energy costs for varying levels of renewable penetration, ranging from 10% to 40% is also explored in this research. In summary, transitioning to renewable energy sources in commercial buildings can reduce environmental impact while ensuring a sustainable energy supply.