Load Flow Research Articles

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.

Power Loss Minimization and Voltage Profile Improvement on Nigeria Distribution Network Using Whale Optimization Algorithm

The power grid has significant power losses, unstable voltage, and large voltage drops during high demand due to its high resistance. One way to reduce power loss is by strategically placing and sizing Distributed Generation (DG) within the distribution network. However, improper installation of DG can increase power loss. To address this issue, various optimization techniques have been used, but finding the best solution and dealing with complex issues has been challenging. It is important to make efforts to optimally place and size DG in the distribution network to minimize power loss. To this end, a research study aimed to minimize power loss and improve the voltage profile on the DG network using the Whale Optimization Algorithm (WOA) was done. Objective functions were formulated and integrated into WOA, and power flow analysis on the standard IEEE 33-bus and 33-bus Ilorin industrial distribution feeder with and without DG was conducted using the forward and backward sweep distribution load flow algorithm. The optimal size and location of DG for power network loss reduction using sensitivity analysis (SA) and the whale optimization algorithm were determined. The results of the power flow analysis indicated that the total active losses and reactive power losses were reduced to varying extents with the integration of DG using both SA and WOA. The validation results showed that WOA is more efficient and provides high-quality solutions in terms of system loss reduction and best placement for DG in power systems compared to the application of SA. Additionally, WOA was found to offer accurate and high-quality solutions for power loss reduction compared to other existing techniques such as Loss Sensitivity Analysis (LSA), Grey Wolf Optimizer (GWO), Adaptive Shuffled Frogs Leaping Algorithm (ASFLA), and One Rank Cuckoo Search Algorithm (ORCSA). Keywords: Optimization, Distributed Generation (DG), Whale Optimization Algorithm (WOA), Sensitivity Analysis (SA)

Reviews on Load Flow Methods in Electric Distribution Networks

Reviews on Load Flow Methods in Electric Distribution Networks

Analysis of contamination and jamming characteristics of fuel metering device based on OMEGA theory

In order to obtain the quantitative law of the influence of pollution particles in fuel on the working characteristics of fuel metering devices and explore an analytical method for the pollution sticking characteristics of non-standard fuel components, the effect of pollution particles on fuel components is converted into sticking force based on the OMEGA theory, and the dynamic model of fuel components and devices under fuel pollution conditions is established. The working characteristics of the fuel metering device when the solid contamination level is GJB450B-7/8/9 is simulated, and the influence of each grade of fuel on the performance of its key components is obtained. The results show that the load flow response obtained by the servo valve contamination sticking model is basically consistent with the results of the standard servo valve contamination sticking model. The maximum sticking force of the metering valve in the 9-grade fuel is about 140 N, the response time is more than twice that of the non-polluting state, and the maximum relative error of the metered fuel quantity increases by more than 4 times. The method proposed in this paper can provide a reference for the pollution sticking modeling of non-standard fuel components and systems.

PMU-based voltage estimation and distributed generation effects in active distribution networks

The integration of distributed generation (DG) and phasor measuring units (PMUs) has significantly impacted electrical distribution networks. This study focuses on real-time control of renewable energy intermittency and strategic PMU deployment. By optimizing the placement of PMUs and DGs, the study aims to achieve several goals: maximize power loss reduction, increase DG penetration, and maintain acceptable voltage profiles. The recommended PMU-based approach incorporates the optimal number of DGs into the distribution network for load flow analysis. Testing this technique on 12-bus, 33-bus, and 69-bus networks yielded substantial gains. For example, power loss was 81.044 % lower in the 33-bus system and 79.256 % lower in the 69-bus test system compared to the base-case scenario. We also compared these results with other methods found in the literature. The developed algorithm is recommended for application in a real electrical power distribution network for more efficient integration of PMUs and new distributed generation units. Integrating PMUs with DG benefits active distribution network management, enabling proactive grid management and stability.

Tectonic evolution of the early permian Junggar basin: Insights into a foreland basin shaped by lithospheric folding

Foreland basins are shaped by the sedimentary architecture and lithospheric flexure due to the sediment load, slab pull, orogenic growth, and asthenospheric mantle flow. However, it is challenging to differentiate foreland basins from rift basins due to the destructive effects of multiphase orogenesis. Situated at the core of the western Central Asian Orogen Belt, the Junggar Basin holds vital clues regarding the closure of the Paleo-Asian Ocean. There is debate regarding whether it was a compressional foreland basin or an extended rift after the closure of the surrounding oceans. Furthermore, uncertainties persist regarding the fate of the residual oceanic crust and the precise tectonic processes during the collision of the ocean-arc-continent system. Understanding the basin's style during the early Permian period is crucial for deciphering its dynamic tectonic history. This study leverages regional seismic data to reevaluate the overall structure of the Junggar Basin, providing insights into its tectonic context and the mechanisms of intraplate deformation during the early Permian period. The findings reveal significant early Permian deformation features characterized by varying strata thicknesses and unconformities, signifying continuous tectonic activity during deposition. The western and southern depressions that developed from the late Carboniferous to early Permian are in keeping with compressional dynamics and reflect a foredeep basin, while the central uplift corresponds to a forebulge. This study posits that the observed foreland basin system was shaped by lithospheric flexure at the end of the late Carboniferous and lithospheric buckling in the early Permian, which contributed to the creation of uplifted Mosuowan-Baijiahai forebulge in the interior. The Junggar Basin featured a convergent dynamic environment, although the surrounding orogen transitioned into a postcollisional orogen in the Central Asian Orogenic Belt during the early Permian.

Abnormal cascading dynamics in transportation networks based on Gaussian distribution of load

In the transportation network, we observe that the distance people travel by means of transportation follows a certain distribution. Statistical analysis shows that people’s travel distance is mainly concentrated in a medium range by the same vehicle, and they choose fewer destinations that are extremely close or far away. However, in previous studies, the impact of distance on the distribution of load flow within the network has often been neglected, or at best, addressed with overly simplistic assumptions. Therefore, we quantify the load flow distribution based on the Gaussian distribution of distances between the nodes. On this basis, a new cascading failure model is proposed using the shortest path strategy to calculate the initial load of the edge. Through the simulation of three real traffic networks and two artificially constructed networks with similar structural characteristics of traffic networks, we found the following interesting anomalies: First, increasing the load-bearing capacity of edges within the network does not necessarily lead to enhanced robustness. Second, we observed that removing more edges does not necessarily lead to a decrease in network robustness; conversely, the network robustness can be higher when a moderate number of edges are removed compared to fewer edges. To better understand the two anomalous dynamics phenomena we observed, we ran simulations on a small-scale network extracted from a real traffic network. We found that, under certain circumstances, the premature failure of some edges may isolate certain regions from the network, which may be responsible for this paradox.

Forecasting material flows of modern logistics centres of railway freight stations exemplifi ed by Xi’an (the People’s Republic of China)

The purpose of the study is to create a more accurate material fl ow forecasting model of Xi’an freight railway station in China. The combined forecasting model is more validated for forecasting freight flows of regional logistics compared to three methods: grey forecasting, Markov chains, entropy weighting. Through the creation of the combined model, the grey forecasting method is combined with Markov chain correction, and the projected data is compared with the actual data to obtain higher accuracy of the forecasting model. A combined model using the grey forecasting method combined with Markov chain correction is created, with the forecast data compared with the actual data to obtain high accuracy of the forecasting model. The practical significance is that in the context of the present post-pandemic economic development, the logistics enterprises that do not operate in accordance with the modern logistics methods may be displaced by competitors. If the railway does not improve its logistics infrastructure, logistics equipment, railway logistics network platform, etc., it will lose out to other modes of transport. In order to meet the needs of logistics and improve the market competitiveness, the main indicator of a freight station is loading and logistics flow. Therefore, exact prediction of future changes in the logistics flow of a freight station can help to determine whether the station needs to be upgraded as a railway station or transformed into a certain type of a logistics centre.

Intricate DG and EV Planning Impact Assessment with Seasonal Variation in a Three-Phase Distribution System

Modern power systems present opportunities and challenges when integrating distributed generation and electric vehicle charging stations into unbalanced distribution networks. The performance and efficiency of both Distributed Generation (DG) and Electric Vehicle (EV) infrastructure are significantly affected by global temperature variation characteristics, which are taken into consideration in this study as it investigates the effects of these integrations. This scenario is further complicated by the unbalanced structure of distribution networks, which introduces inequalities that can enhance complexity and adverse effects. This paper analyzes the manner in which temperature changes influence the network operational voltage profile, power quality, energy losses, greenhouse harmful emissions, cost factor, and active and reactive power losses using analytical and heuristic techniques in the IEEE 69 bus network in both three-phase balance and modified unbalanced load conditions. In order to maximize adaptability and efficiency while minimizing the adverse impacts on the unbalanced distribution system, the findings demonstrate significant variables to take into account while locating the optimal location and size of DG and EV charging stations. To figure out the objective, three-phase distribution load flow is utilized by the particle swarm optimization technique. Greenhouse gas emissions dropped by 61.4%, 64.5%, and 60.98% in each of the three temperature case circumstances, while in the modified unbalanced condition, they dropped by 57.55%, 60.39%, and 62.79%. In balanced conditions, energy loss costs are reduced by 95.96%, 96.01%, and 96.05%, but in unbalanced conditions, they are reduced by 91.79%, 92.06%, and 92.46%. The outcomes provide valuable facts that electricity companies, decision-makers, along with other energy sector stakeholders may utilize to formulate strategies that adapt to the fluctuating patterns of electricity distribution during fluctuations in global temperature under balanced and unbalanced conditions of network.

Nonlinear flow modeling of electro hydrostatic pump unit based on Gauss Newton iterative method for high performance control

For the electric hydrostatic pump unit (EPU), which is a crucial component in the electro hydraulic servo pump control system (EHSPCS), nonlinear flow modelling is a key challenge to achieve high performance operation, especially in position control. For the nonlinear flow of EPUs, simple linearization combined with compensation has become a popular control method recently. However, the control performance is constrained by the reliance on deviation correction. This paper proposes utilizing the Gauss Newton iterative method to investigate the nonlinear flow modeling in the EPU, aiming to obtain the mapping relationship between the load flow and operating parameters in the EPU, thereby directly improving the control performance. A nonlinear flow model is built by utilizing the Gauss Newton iterative method based on test data obtained from various operating conditions, and the mapping relationship is presented accordingly. Experimental studies show that compared to well-established methods, this model exhibits high accuracy and practicality in improving the control performance, particularly in position control of the EPU.

Robust Optimization Research of Cyber-Physical Power System Considering Wind Power Uncertainty and Coupled Relationship.

To mitigate the impact of wind power uncertainty and power-communication coupling on the robustness of a new power system, a bi-level mixed-integer robust optimization strategy is proposed. Firstly, a coupled network model is constructed based on complex network theory, taking into account the coupled relationship of energy supply and control dependencies between the power and communication networks. Next, a bi-level mixed-integer robust optimization model is developed to improve power system resilience, incorporating constraints related to the coupling strength, electrical characteristics, and traffic characteristics of the information network. The upper-level model seeks to minimize load shedding by optimizing DC power flow using fuzzy chance constraints, thereby reducing the risk of power imbalances caused by random fluctuations in wind power generation. Furthermore, the deterministic power balance constraints are relaxed into inequality constraints that account for wind power forecasting errors through fuzzy variables. The lower-level model focuses on minimizing traffic load shedding by establishing a topology-function-constrained information network traffic model based on the maximum flow principle in graph theory, thereby improving the efficiency of network flow transmission. Finally, a modified IEEE 39-bus test system with intermittent wind power is used as a case study. Random attack simulations demonstrate that, under the highest link failure rate and wind power penetration, Model 2 outperforms Model 1 by reducing the load loss ratio by 23.6% and improving the node survival ratio by 5.3%.

On the Uniqueness of the Solution of Electric Power System Load Flow Nonlinear Equation Systems in the Form of a Power Balance

On the Uniqueness of the Solution of Electric Power System Load Flow Nonlinear Equation Systems in the Form of a Power Balance

Impacts of Natural Gas Pipeline Congestion on the Integrated Gas–Electricity Market in Peru

This paper investigates the impact of natural gas pipeline congestion on the integrated gas–electricity market in Peru, focusing on short-term market dynamics. By simulating congestion by reducing the primary natural gas pipeline’s capacity, the study reveals significant patterns in production costs and load flows within the electrical network. The research highlights the critical interdependencies between natural gas and electricity systems, emphasizing how constraints in one network can directly affect the other. The findings underscore the importance of coordinated management of these interconnected systems to optimize economic dispatch and ensure the reliability of both gas and electricity grids. The study also proposes strategic public policy interventions to mitigate the financial and physical impacts of pipeline congestion, contributing to more efficient and resilient energy market operations.

Quality by design for mRNA platform purification based on continuous oligo-dT chromatography

Oligo-deoxythymidine (Oligo-dT) ligand-based affinity chromatography is a robust method for purifying mRNA drug substances within the manufacturing process of mRNA-based products, including vaccines and therapeutics. However, the conventional batch mode of operation for oligo-dT chromatography has certain drawbacks that reduce this process’s productivity. Here, we report a new continuous oligo-dT chromatography process for the purification of in vitro transcribed mRNA, which reduces losses, improves the efficiency of oligo-dT resin use, and intensifies the chromatography process. Furthermore, the quality by design (QbD) framework was used to establish a design space for the newly developed method. The optimisation of process parameters, including salt type, salt concentration, load flow rate and mRNA load concentration both in batch and the continuous mode, achieved >90% yield (mRNA recovery) along with >95% mRNA integrity and >99% purity. The productivity of continuous chromatography was estimated to be 5.75-fold higher, and the operating cost was estimated 15% lower, when compared to batch chromatography. Furthermore, the QbD framework was further used to map the relationship between critical quality attributes (CQAs) and key performance indicators (KPIs) as a function of critical process parameters (CPPs) and critical material attributes (CMAs).

Power Quality Enhancement in High-Voltage Transmission Systems Using STATCOM by Type 1 and Type 2 Fuzzy Logic Controllers

The continuing growth of load demand leads to increased power system complexity and interconnections. During faults and abnormal operating conditions, conventional power systems change the load flow in transmission lines, which may cause voltage drops or swells, and other power quality issues. Voltage regulation is connected to reactive power compensation in system busbars, and fixed capacitor banks are usually used for this purpose, but they are not flexible and dynamic enough to meet the power quality requirements. With power electronics development, flexible alternative current transmission systems (FACTS) became a popular solution for power quality improvement and power transmission capability increment in power systems, and FACTS can be categorized into series and shunt systems. In this study, STATCOM was used to improve the voltage profile in part of Iraq’s high-voltage transmission system during abnormal operating conditions, such as sudden load addition and removal, which causes voltage dip and voltage swell. Type 1 fuzzy logic controller and type 2 fuzzy logic controller were used as the outer controller in the STATCOM; both controllers were able to regulate the voltage magnitude by reducing the voltage dip from 1.32 to 0.4%, voltage swell from 1.02 to 0.5%, and voltage THD at the connecting point did not exceed 3%, indicating that SATACOM with FLC improves the voltage profile under different operating conditions.

Enrichment of secoiridoid glycosides from Gentiana rigescens extracts in amino acid-based deep eutectic solvents via macroporous resin adsorption

The simultaneous enrichment of the secoiridoid glycosides gentiopicroside, loganic acid, swertiamaroside, and sweroside from amino acid-based deep eutectic solvent (AADES) extracts of Gentiana rigescens roots and rhizomes was developed. To screen suitable resins, the performance and characterization of 11 widely used macroporous resins for the adsorption and desorption of target secoiridoid glycosides were considered. Compared with the other resins, the H103 resin was found to have greater adsorption and desorption capacities for gentiopicroside, loganic acid, swertiamaroside, and sweroside. Therefore, this resin was chosen as the best resin for secoiridoid glycosides enrichment. In a column packed with H103 resin, the dynamic adsorption and desorption factors for the target secoiridoid glycosides were investigated and optimized. After dynamic testing on the H103 packed column, the optimal operating conditions were determined as follows: a sample volume of 12 bed volumes (BVs) and a loading flow rate of 2 BV/h were used, the volume of deionized water used for the aqueous washing process was 8 BV, and the aqueous washing flow rate was 3 BV/h. Ethanol with a volume fraction of 10–90 % was used as the eluent in the ethanol desorption process with a gradient of 10 %. Each ethanol volume fraction was used for 2 BV, with a flow rate of 3 BV/h. The eluent fractions containing 40–80 % ethanol were collected, combined, and concentrated, followed by vacuum drying to obtain the final product. The purities of gentiopicroside, loganic acid, swertiamaroside, and sweroside in the final product were 58.11, 13.73, 3.86, and 2.77 %, respectively, with corresponding recovery rates of 74.09, 72.19, 77.50, and 76.45 %, respectively. Additionally, the reuse of AADES proved to be effective.

Mesoporous silica-based amorphous solid dispersions to enhance the oral bioavailability of Neratinib maleate

Neratinib maleate (NM) used in the treatment of breast cancer, has a low and variable oral bioavailability due to its pH-dependent solubility and gut-wall metabolism-restricted permeability. Mesoporous silica based solid dispersion of NM (SD-NM-MSi) was designed to improve its solubility and dissolution rate. SYLOID XDP 3050 was selected as a mesoporous silica carrier based on its higher drug loading (15.7 %) and good flow properties. The extensive surface area of the Mesoporous silica offered a suitable site for drug adsorption. Thermal and diffractometry analysis indicated that NM was adsorbed in an amorphous form. In vitro dissolution studies revealed that the optimized SD-NM-MSi showed a higher rate and extent of drug dissolution compared to plain NM in pH 3.0, pH 4.5, and 6.8, respectively. Similarly, an oral pharmacokinetic study (10 mg/kg) resulted in significantly higher plasma exposure for SD-NM-MSi compared to plain NM, with a 73 % (P < 0.01) increase in the relative oral bioavailability of NM.

Integrated Optimal Energy Management of Multi-Microgrid Network Considering Energy Performance Index: Global Chance-Constrained Programming Framework

Distributed generation (DG) sources play a special role in the operation of active energy networks. The microgrid (MG) is known as a suitable substrate for the development and installation of DGs. However, the future of energy distribution networks will consist of more interconnected and complex MGs, called multi-microgrid (MMG) networks. Therefore, energy management in such an energy system is a major challenge for distribution network operators. This paper presents a new energy management method for the MMG network in the presence of battery storage, renewable sources, and demand response (DR) programs. To show the performance of each connected MG’s inefficient utilization of its available generation capacity, an index called unused power capacity (UPC) is defined, which indicates the availability and individual performance of each MG. The uncertainties associated with load and the power output of wind and solar sources are handled by employing the chance-constrained programming (CCP) optimization framework in the MMG energy management model. The proposed CCP ensures the safe operation of the system at the desired confidence level by involving various uncertainties in the problem while optimizing operating costs under Mixed-Integer Linear Programming (MILP). The proposed energy management model is assessed on a sample network concerning DC power flow limitations. The procured power of each MG and power exchanges at the distribution network level are investigated and discussed.

Hi‐Z fault location identification on low voltage distribution systems using IoT‐based digital twin model computation

AbstractDistribution feeders carry and supply power to industrial, commercial and residential loads from the point where sub‐transmission (33 kV level) ends after stepping down to suitable voltages, such as 11 kV and further down to 400/230 V. In recent times, high impedance (Hi‐Z) faults on distribution systems are creating unique challenges to utilities both from operational and safety perspectives. Most of these Hi‐Z faults occur at distribution voltages of 15 kV or below, with the problem being worse at lower voltages. Hi‐Z fault detection technologies emerged and were developed and incorporated on embedded platforms, such as relays, reclosers and sensors, which protect and monitor distribution systems. Although these technologies can detect Hi‐Z fault on feeders, most of them cannot identify the exact location of the fault. Specifically, there is no solution available in literature for detecting Hi‐Z fault location on low voltage (LV) circuits like 3‐phase 4‐wire 400 V distribution network. In this paper, we introduce a novel and a unique algorithm to identify the location of Hi‐Z faults using proposed smart metres IoT data‐based distribution system load flow and digital twin model representation of the network. Furthermore, a case study on the standard 33 bus LV system clearly depicts the functionality of the proposed algorithm.