Residential Distribution Research Articles

Is There Really a Difference in Outcomes between Men and Women with Hepatocellular Cancer?

Hepatocellular carcinoma (HCC) is a male-dominated disease. Currently, gender differences remain incompletely defined. Data from the state tumor registry were used to investigate differences in demographics, comorbidities, treatment patterns, and cancer-specific survival (HSS) among HCC patients according to gender. Additional analyses were performed to evaluate racial differences among women with HCC. 2627 patients with HCC were included; 498 (19%) were women. Women were mostly white (58%) or African American (39%)-only 3.8% were of another or unknown race. Women were older (65.1 vs. 61.3 years), more obese (33.7% vs. 24.2%), and diagnosed at an earlier stage (31.7% vs. 28.4%) than men. Women had a lower incidence of liver associated comorbidities (36.1% vs. 43%), and more often underwent liver-directed surgery (LDS; 27.5% vs. 22%). When controlling for LDS, no survival differences were observed between genders. African American women had similar HSS rates compared to white women (HR 1.14 (0.91,1.41), p = 0.239) despite having different residential and treatment geographical distributions. African American race and age >65 were predictive for worse HSS in men, but not in women. Overall, women with HCC undergo more treatment options-likely because of the earlier stage of the cancer and/or less severe underlying liver disease. However, when controlling for similar stages and treatments, HCC treatment outcomes were similar between men and women. African American race did not appear to influence outcomes among women with HCC as it did in men.

Electrical fire risk indexing using fuzzy Petri nets

Electrical fires are a significant cause of dwelling fires, but the existing information on electrical fires is often vague and imprecise, making it challenging to develop a comprehensive risk assessment method. To address this problem, this paper proposes a risk assessment method based on Fuzzy Petri nets (FPNs), a modeling tool that is suitable for complex systems under uncertainty. This approach allows for modeling various relationships between risk factors and their respective importance, supplying a flexible and comprehensive way to perform risk assessment. The proposed method involves responding to a yes/no questionnaire based on code recommendations, which allows for the prediction of the failure mode response. Based on the simulation results, it can be evaluated the overall risk, determined the relevance of each electrical issue in a residential distribution system, or assessed the impact of electrical installation improvements and alterations on safety. Despite the complexity of failure modes modeling, this approach offered a practical and straightforward means of identifying potential electrical fire risks.

Optimal scheduling algorithm for residential building distributed energy source systems using Levy flight and chaos-assisted artificial rabbits optimizer

The increase in demand for MicroGrids (MGs) is a significant factor in the provision of electricity in the future, mainly due to the use of renewable energy sources, which reduces the release of hazardous gases. The grid-connected MG operation is the most cost-effective and reliable because it actively involves the grid buying and selling power, lowering the electricity cost of the MG. This study describes a residential thermal/electrical home energy system comprising a battery energy storage system and a combined heat and power fuel cell. The optimal planning of various energy resources is scheduled by a new optimization algorithm called Levy Flight and Chaos-assisted Artificial Rabbits Optimization (LFCARO), resulting in the lowest operational cost of this combined power system. The operating cost of a residential building is reduced by using a day-ahead scheduling process for controlling multiple energy sources to create a reliable look-up table that estimates the best schedule for the distributed energy sources at each time frame. The impact of various electricity prices for obtaining energy from the primary grid on the system’s operating costs is examined. The efficiency of LFCARO is compared with other algorithms, and the results show that LFCARO performs better than other algorithms. The execution time of the proposed LFCARO is less than 1 sec. for 10 numerical problems and less than 1.5 sec. for the resource scheduling of residential distribution systems. Based on the average Friedman’s ranking test values, the proposed algorithm stands first with 1.82 for numerical and real-world scheduling problems.

Rule-Based Peak Shaving Using Master-Slave Level Optimization in a Diesel Generator Supplied Microgrid

This paper proposes a rule-based peak shaving method using master-slave level optimization and presents its impact on an isolated diesel generator (DG) supplied microgrid. The DG is connected to low voltage (LV) ac bus through a back-to-back converter. This DG supplies power to a modified CIGRE residential distribution system which consists of residential loads and renewable energy sources (RESs). In such a system the fuel consumption of DG not only depends on the LV ac bus power requirement but also on its rated power. The rated power of DG can be reduced through the peak shaving application. In this scenario, a battery energy storage system (BESS) is connected at dc bus of the back-to-back converter for the peak shaving of DG power. The rule-based peak shaving method is proposed for determining BESS charge/discharge schedules considering the day-ahead LV ac bus powers over a day as inputs. For that firstly the LV ac bus powers are optimally obtained using a slave level optimization. In order to obtain the optimal peak DG power a master level optimization is used which determines the optimal control inputs for the rule-based control. The proposed peak shaving control method is tested in MATLAB.

A Reinforcement Learning Approach for Integrating an Intelligent Home Energy Management System with a Vehicle-to-Home Unit

These days, users consume more electricity during peak hours, and electricity prices are typically higher between 3:00 p.m. and 11:00 p.m. If electric vehicle (EV) charging occurs during the same hours, the impact on residential distribution networks increases. Thus, home energy management systems (HEMS) have been introduced to manage the energy demand among households and EVs in residential distribution networks, such as a smart micro-grid (MG). Moreover, HEMS can efficiently manage renewable energy sources, such as solar photovoltaic (PV) panels, wind turbines, and vehicle energy storage. Until now, no HEMS has intelligently coordinated the uncertainty of smart MG elements. This paper investigated the impact of PV solar power, MG storage, and EVs on the maximum solar radiation hours. Several deep learning (DL) algorithms were utilized to account for the uncertainties. A reinforcement learning home centralized photovoltaic (RL-HCPV) scheduling algorithm was developed to manage the energy demand between the smart MG elements. The RL-HCPV system was modelled according to several constraints to meet household electricity demands in sunny and cloudy weather. Additionally, simulations demonstrated how the proposed RL-HCPV system could incorporate uncertainty, and efficiently handle the demand response and how vehicle-to-home (V2H) can help to level the appliance load profile and reduce power consumption costs with sustainable power production. The results demonstrated the advantages of utilizing RL and V2H technology as potential smart building storage technology.

Low carbon economic scheduling of residential distribution network based on multi-dimensional network integration

This paper focuses on the research of the residential distribution network and take the community as the research object. With the structure of energy supply and consumption of the residential distribution network gradually shows the characteristics of diversity, based on the integrated energy system of electricity, heat, and natural gas network, the power consumption of the mobile communication network, the orderly charging and discharging behavior of electric vehicles and carbon emission limit are also considered. Load aggregator is introduced as the energy manager to arrange the energy purchase and schedule strategy. Taking the minimization of energy cost of the residential distribution network as the optimization goal, a low-carbon economic scheduling model of the residential distribution network based on multi-dimensional network integration is established. Numerical examples are simulated based on the data of an actual community in Shanghai. Simulation results show that the model built in this paper can effectively reduce the energy cost and carbon emission of the community, and the daily load curve is significantly smoothed by shaving peak load and filling valley load, furthermore, it can improve the flexibility of energy consumption in the community, and reduce the operation pressure of the distribution transformer, promote the cost-effective, low-carbon, and reliable operation of the residential distribution network.

Electric Vehicle Smart Charging Coordination on a Residential Distribution Grid

Electric Vehicle Smart Charging Coordination on a Residential Distribution Grid

A quantile panel examination of the moderation effects of guardianship on residential burglary.

Do informal and formal mechanisms of guardianship work together to reduce residential burglary? In this article we argue that informal guardianship moderates the relationship between formal mechanisms of guardianship and residential burglary. Formal guardianship requires some level of social cohesion and trust to be effective against residential burglary. We test this argument with the use of robust panel quantile methods controlling for time effects, spatial effects, and alternative explanations. Using Mexico City neighborhood crime and census data, we show evidence of a moderating weakening effect of informal guardianship on the previous relationship, particularly in deprived neighborhoods and only in the upper quantiles of the residential burglary distribution. In addition, the moderation effects seem to have weakened over time. In sum, the combination of guardianship mechanisms seems to have been more effective in high burglary risk deprived neighborhoods, although their combination seems to have become less relevant.

Analysis of the spatial inequality in residential quality indicators: the 22 urban regions of Tehran Metropolis

Research objective: the present study seeks to assess and analyze the status of residential quality indicators in the 22 urban regions of the Tehran metropolis.
 Methodology: 79 residential quality variables are classified into the four indicators of physical, economic, ecological, and socio-cultural indices. Shannon entropy model, the coefficient of variation, TOPSIS, hotspot analysis, and Moran spatial autocorrelation were used for data analysis.
 Results: Results of the present study indicated a TOPSIS score of 0.58 for the integrated indicators which revealed region 1 had the best and region 15 had the worst status. In fact, a sort of divergence and heterogeneity rules residential quality indicators in Tehran urban regions, so that among the 22 urban regions of the city, around 4.5% were advantaged while 13.6% were relatively advantaged, 77.3% were semi- advantaged, and 4.5% were less advantaged. Results of statistical spatial analysis also indicate that the advantaged clusters were located in northern and northeastern parts of the city while the less advantaged and extremely less advantaged clusters were more focused in the southern and southeastern parts of the city, and residential quality indicators followed a cluster pattern.
 Conclusion: A spatial difference and inequality in residential quality index distribution were observed in the 22 urban regions of Tehran so that the regions located in the southern and southeastern parts of the city had an unfavorable status in terms of residential quality indicators while the northern and northeastern regions were more advantaged in this regard.

Multi-Objective-Based Charging and Discharging Coordination of Plug-in Electric Vehicle Integrating Capacitor and OLTC

The integration of plug-in electric vehicles (PEVs) in residential distribution networks demands a significant amount of electrical load where random and uncoordinated charging affects the quality and performance of the distribution network. Random and uncoordinated charging may increase the peak demand and can increase stress on critical network assets such as line, transformer, and switching devices. Moreover, the charging of PEVs in a low network reduces the voltage of the system below the lower limit. On the other hand, using PEVs as storage in the V2G mode can improve the network condition. Therefore, it is critical to properly manage the charging and discharging operation of PEVs. This paper proposes a multi-objective-based charging and discharging coordination of PEVs with the operation of the capacitor and on-load tap changer (OLTC). With the proposed strategy, the distribution network is operated safely, and charging is ensured for all PEVs connected to the network. The main consideration of this research is to reduce the daily power loss, operational cost, and voltage deviation of the system. The metaheuristic optimization binary firefly algorithm (BFA) has been applied to coordinate PEV charging and discharging as well as capacitor and OLTC operation in the system. A modified IEEE 31 bus 23 kV distribution system is used to implement the proposed strategy. From the obtained results, it is found that the combined PEV charging and discharging coordination with capacitor and OLTC operation reduces the power loss and cost by 34.16% and 12.68%, respectively, with respect to uncoordinated charging and enhances the voltage condition of the network.

Residential Harmonic Injection Models Based on Field Measurements

In recent years, harmonic current levels have increased in residential distribution networks due to the growing penetration of nonlinear loads at customer premises. To properly model the harmonic current injection of residences, probabilistic models that account for the stochastic behavior of domestic loads are needed. As a part of the study reported in this paper, a field measurement campaign was performed in 24 dwellings during a one-week period with a one-minute resolution. Harmonic current magnitudes and phase angles of odd harmonic orders up to the 25th as well as the active and reactive power demanded were recorded. By applying two different unsupervised learning techniques (i.e., nonparametric density estimation and Gaussian mixture models) to these measurements, two probabilistic models of harmonic injection of individual residential sites have been derived. Statistical probability distributions have been determined for the magnitude and phase of each harmonic order segmented in different power demand intervals. The developed models are validated on a test feeder by comparing harmonic voltages caused by the injection of the synthetically generated currents with those obtained from measurements. Both field measurement results and the detailed data defining the probabilistic harmonic current models are made public in an Open Science database repository.

Impact of Electric Vehicle on Residential Power Distribution Considering Energy Management Strategy and Stochastic Monte Carlo Algorithm

The area of a Microgrid (μG) is a very fast-growing and promising system for overcoming power barriers. This paper examines the impacts of a microgrid system considering Electric Vehicle Grid Integration (EVGI) based on stochastic metaheuristic methods. One of the biggest challenges to slowing down global climate change is the transition to sustainable mobility. Renewable Energy Sources (RESs) integrated with Evs are considered a solution for the power and environmental issues needed to achieve Sustainable Development Goal Seven (SDG7) and Climate Action Goal 13 (CAG13). The aforementioned goals can be achieved by coupling Evs with the utility grid and other RESs using Vehicle-to-Grid (V2G) technology to form a hybrid system. Overloading is a challenge due to the unknown number of loads (unknown number of Evs). Thus, this study helps to establish the system impact of the uncertainties (arrival and departure Evs) by proposing Stochastic Monte Carlo Method (SMCM) to be addressed. The main objective of this research is to size the system configurations using a metaheuristic algorithm and analyze the impact of an uncertain number of Evs on the distribution of residential power in Tripoli-Libya to gain a cost-effective, reliable, and renewable system. The Improved Antlion Optimization (IALO) algorithm is an optimization technique used for determining the optimal number of configurations of the hybrid system considering multiple sources, while the Rule-Based Energy Management Strategy (RB-EMS) controlling algorithm is used to control the flow of power in the electric power system. The sensitivity analysis of the effect parameters has been taken into account to assess the expected impact in the future. The results obtained from the sizing, controlling, and sensitivity analyses are discussed.

Evolving pipe joining methods and their association to musculoskeletal symptoms for residential plumbers.

Recently the plumbing trade has transitioned from traditional copper piping to flexible plastic piping (PEX) for residential water distribution systems. However, there has been very limited research into the ergonomic implications of the modernized processes. This research documents the physical workload and risks of musculoskeletal disorders (MSDs) with the use of new tools and processes for joining piping. The research also identifies the factors which can facilitate or limit the use of new ergonomically beneficial tools. This mixed methods research included workplace observations, interviews, an experiment, a survey of plumbers in residential construction and focus groups with both plumbers and plumbing contractors. Advantages and disadvantages of the various techniques for joining pipes showed that manual crimping has advantages (i.e., productivity and lower cost) that make it desirable for plumbing contractors. Power devices, which were not widely used, have great potential to reduce MSD risks especially if the size and weight of the tools decreases with newer technologies. A continuing barrier is the cost of power equipment. The move to provide ergonomically beneficial tools was not as rapid as the willingness to change piping materials. Productivity and costs of tools are barriers to ergonomic interventions.

Critical review on integration of electric vehicles into residential distribution system

This paper presents a literature survey on various issues related to Electric Vehicles (EV) from an Electric Power Systems (EPS) perspective. For the past few years, the EPS has been transforming exceptionally, necessitating a substantial change in the network configuration, operation, and control of conventional power systems. Especially, distribution networks have undergone a significant transformation. Moreover, new loads like Electric Vehicles are going to be integrated with the electrical grid, and these EV loads will modify the consumption of electrical energy. Electric Vehicles will have a great impact on the distribution network because of their nonlinear behavior. At present, most of the existing EVs are connected to the residential distribution network for charging, which usually increases the demand for residential power. Consequently, unplanned charging of a huge number of EVs will result in an excessive energy demand that will result in several issues, such as power outages, voltage fluctuations, thermal stress on the lines, and harmonic pollution.

An Optimal Distributed RES Sizing Strategy in Hybrid Low Voltage Networks Focused on EVs’ Integration

The new era of the energy sector has already begun, and therefore, new challenges need to be tackled. A major challenge that residential distribution grids are going to encounter with the integration of photovoltaic (PV) panels and electric vehicles (EVs) is the unsynchronized new demand and the time-limited production of distributed generation, in combination with space limitations. Therefore, the necessity of energy storage systems (ESSs) is more than evident. ESSs have excessive manufacturing costs, implying that the purchase cost for residential users can be prohibitive. In the present work, a distributed optimal small-scale PV energy system sizing strategy is proposed, by considering the individual energy needs of each residence and their EVs. The strategy is formulated based on the demand of the households and EVs charging. By enabling the fuzzy cognitive maps theory, a graph is designed, aiming to establish the correlation among the individual energy parameters and the characteristics of the renewable energy sources (RES). The optimization results reveal that the adopted hybrid approach can reduce the energy cost significantly, up to almost 40%, while enabling distribution system operators (DSOs) to incorporate additional loads, without the need for network expansion. Finally, based on the extracted results, a short discussion about the concept of EVs’ charging by residential RES is presented.

Harmonic Analysis of Different Types of EV Chargers on Residential Distribution Systems

Harmonic Analysis of Different Types of EV Chargers on Residential Distribution Systems

Polytetrafluoroethylene-based gas diffusion electrode for electrochemical generation of hydrogen peroxide

Hydrogen peroxide (H2O2) is a versatile oxidizing agent with numerous applications in various industries. One application of H2O2 is for water treatment, where it is combined with an ultraviolet (UV) radiation source to produce hydroxyl radicals. For point-of-use water treatment devices, the in-situ electrochemical generation of H2O2 is a more attractive solution compared with the external addition of chemicals. One common electrode for H2O2 production is gas diffusion electrode (GDE), which facilitates oxygen transport to the reaction zone. In our study, a GDE was developed based on an expanded polytetrafluoroethylene (ePTFE) substrate for the in-situ cathodic production of H2O2. The GDE characterization indicated its longevity and tolerance to a pressure difference of more than 70 psi, which is typical in residential water distribution systems. The performance of the GDE was evaluated in an electrochemical cell based on various parameters. The cell generated ∼0.6 mM H2O2 at a catalyst loading of 0.22 mg cm–2, flow rate of 40 mL min–1, and current density of ∼200 A m–2. When coupled with a UV-LED array, the ePTFE-based cell was able to oxidize >60% of methylene blue. To the best of our knowledge, this is the first study in which an ePTFE-based GDE has been applied to the electrochemical generation of H2O2, with the ePTFE network as the gas diffusion layer (GDL), thus making the system practically leak-free. The air-breathing electrochemical cell with the ePTFE-based GDE is a reliable means of generating H2O2.

Statistical analysis of PV penetration impact on residential distribution grids

This paper presents a comprehensive approach to the probabilistic analysis of residential distribution grid injected by distributed PV sources. The approach is data-driven and is able to deal with the general scenario that includes the uncertainty of correlated PV sources and of statistically independent consumer loads. The novel method adopts a Gaussian-Mixture-Model for correctly representing PV sources correlation and a fresh probabilistic analysis technique employing Multi-Expansion polynomial chaos. Numerical experiments carried out on the Non-Synthetic European low voltage test system, highlight the importance of the comprehensive modeling strategy in order to realistically quantify uncertainty impact on the grid.

Improving urban seismic risk estimates for Bishkek, Kyrgyzstan, through incorporating recently gained geological knowledge of hazards

Many cities are built on or near active faults, which pose seismic hazard and risk to the urban population. This risk is exacerbated by city expansion, which may obscure signs of active faulting. Here, we estimate the risk to Bishkek city, Kyrgyzstan, due to realistic earthquake scenarios based on historic earthquakes in the region and an improved knowledge of the active fault sources. We use previous literature and fault mapping, combined with new high-resolution digital elevation models to identify and characterise faults that pose a risk to Bishkek. We then estimate the hazard (ground shaking), damage to residential buildings and distribution of losses (economical cost and fatalities) using the Global Earthquake Model OpenQuake engine. We model historical events and hypothetical events on a variety of faults that could plausibly host significant earthquakes. This includes proximal, recognised, faults as well as a fault under folding in the north of the city that we identify using satellite DEMs. We find that potential earthquakes on faults nearest to Bishkek—Issyk Ata, Shamsi Tunduk, Chonkurchak and the northern fault—would cause the most damage to the city. An Mw 7.5 earthquake on the Issyk Ata fault could potentially cause 7900 ± 2600 completely damaged buildings, a further 16,400 ± 2000 damaged buildings and 2400 ± 1500 fatalities. It is vital to properly identify, characterise and model active faults near cities to reduce uncertainty as modelling the northern fault as a Mw 6.5 instead of Mw 6.0 would result in 37% more completely damaged buildings and 48% more fatalities.

Integration of Solar Energy Supply on Smart Distribution Board Based on IoT System

Today’s residential electricity management depends heavily on the Internet of Things (IoT). It is still challenging to create practical, affordable smart condition monitoring, protection, and control systems for residential distribution networks. The performance of the developed system is evaluated for a variety of residential electric loads with different energy consumption patterns. In this paper, a photovoltaic tracking system and also a cleaning system will be connected to a smart board. All input data will be gathered and sent over Bluetooth using a working prototype built on an Arduino board. The mobile application is also used to let individual users monitor the system in real-time, alert users about electrical system issues, display their energy use in real time, and provide other essential alerts and warnings.