Domestic Load Research Articles

Sewage leakage challenges urban wastewater management as evidenced by the Yangtze River basin of China

Sewage leakage, including uncollected, collected but exfiltrated, and collected but discharged with stormwater in combined sewer overflows, becomes a key challenge to clean water around the world. This study proposed a mass balance model to quantify sewage leakage and analyzed its influencing factors. Approximately 56% (53%–59%) of domestic total nitrogen loads in the Yangtze River basin of China leak out without treatment, two-thirds are exfiltrated or overflowed, significantly impacting receiving water quality. Cities with higher precipitation have greater leakage, indicating overflows are important. Pipe density has limited impact, showing higher importance of pipe quality over quantity. The per capita leakage rate first increases with city size and then decreases, showing the economies of scale. Yet uneven distribution of population and economy within megacities leads to greater leakage. Hence, separated sewage systems of rainwater and wastewater, regular maintenance and adapting centralized and decentralized systems are recommended, tailored to city-specific leakage characteristics.

Series arc‐fault diagnosis using convolutional neural network via generalized S‐transform and power spectral density

AbstractIt is difficult to identify an arc fault accurately when the loads on the user side are more complicated, which hinders the development of low‐voltage monitoring and pre‐warning inspection. This study acquired a series of arc‐fault signals according to IEC 62606. The main time‐frequency features were strengthened with high efficiency by applying the generalized S‐transform to them with a bi‐Gaussian window. Further, the power spectrum density determination allowed for the detection of imperceptible high‐frequency harmonic energy reflections, thus increasing the rate of arc‐fault diagnosis and making it suitable for arc‐fault monitoring of non‐linear loads. The final samples were trained and classified using a 2D convolutional neural network and the overall accuracy of identification was observed to be 98.13%, which involved various domestic loads, thus providing a reference for follow‐up arc‐fault monitoring and inspection research.

A Dual control strategy for improved power quality in grid-tied off-board bidirectional electric vehicle charger

Electric vehicle (EV) chargers face significant challenges in maintaining grid power quality (PQ) and ensuring efficient power management during grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operations. Additionally, they must seamlessly switch to vehicle-to-load (V2L) mode during grid disturbances to provide uninterrupted power supply (UPS) for domestic loads. This article explores a dual control approach incorporating adaptive model predictive direct power control (AMP-DPC) on the rectifier side and adaptive direct power control (ADPC) on the dual active bridge (DAB) side to address these challenges. The AMP-DPC employs a control strategy referred to the second-order generalized integrator (SOGI) which estimate synchronizing voltage templates specifically designed for single-phase systems. The proposed optimization control aims to mitigate the cost function value by selecting appropriate switching modes. The optimized function is independent of the weighting factor, expressing the optimal modulation function across various weighting factors without additional design or selection. The current reference used by the charger is designed to ensure that the power factor remains at closer unity throughout G2V and V2G modes. The proposed control algorithm ensures the grid current remains low in harmonics during G2V, V2G, and V2L modes of operation. The experimental validation of the proposed control strategy is performed in the laboratory on a 0.5 kW off-board electric vehicle charger (EVC) prototype under various conditions to demonstrate its effectiveness in compliance with the IEEE 519–2022 standard.

A Champernowne Adaptive Filter-Based Control of Grid Interactive Solar PV with Power Quality Enrichment

This work presents a variable step Champernowne Adaptive Filter (VS-CAF) based control architecture for a grid-integrated solar photovoltaic system. The control structure is implemented to successfully synchronize the two-stage solar photovoltaic at the point of common coupling where the source and domestic loads are connected. The control architecture ensures solar power injection and improvised power quality at the unity power factor. Moreover, the control structure provides only the balanced positive sequence fundamental sinusoidal source currents at solar irradiation alteration and load disturbances. The VS-CAF is exploited to estimate the peak of the fundamental component out of the harmonics-rich domestic load currents. To examine the efficacy of the suggested control technique, a 40 kW solar photovoltaic is simulated using MATLAB/Simulink. The performance assessment of the grid-tied solar generation system is carried out at solar irradiation alteration, zero solar irradiation, and detachment of one phase of the three-phase load. Various simulation results are carried out to verify the successful integration of solar photovoltaics into the three-phase grid following the IEEE-519 standard and satisfactory performance during various working conditions. Moreover, the simulation results are also verified with real-time results taken from the Opal-RT simulator.

Investigation of the energy performance of thermal energy sharing for photovoltaic-thermal systems in a thermal prosumer network

This study analyzes the potential for energy saving through the integration of a photovoltaic-thermal (PVT) system with a heat pump (HP) in a community with a small-scale thermal network. An empirical study was conducted in a community to analyze and compare the performance of a HP and small-scale thermal network with hub thermal energy storage system that utilizes the surplus thermal energy produced by a PVT system installed in a residential house from other buildings with that of an existing electric hot water boiler. The energy saving analysis of the proposed system revealed that the energy consumption could be reduced by more than 70 % compared to the existing system. The HP exhibited a COP ranging between 4.2 and 4.7. Throughout the experiment, the integrated system collected an average of 23.77 kWh of heat energy daily from the PVT units. The annual domestic hot water load coverage from PVT through the thermal network was found to be 50.4 %. These results indicate that PVT systems combined with thermal networks and HPs can effectively reduce energy consumption in individual buildings and enhance energy efficiency within communities.

Efficient energy management of domestic loads with electric vehicles by optimal scheduling of solar-powered battery energy storage system

The increasing adoption of electric vehicles (EVs) and variable energy usage patterns substantially strain the electrical grid; indeed, optimal energy management, monitoring, and utilization are required for the reliable operation of the grid. This paper introduces a novel model design of a solar-powered battery energy storage system (SPBESS) as a viable substitute for conventional demand-side management (DSM) and time of use (ToU) pricing schemes, intending to optimize energy management and utilization with IoT monitoring. In addition, the IoT-based prototype has been developed to monitor and control the proposed system. To validate the proposed SPBESS model, the study examines two distinct cases: one encompassing domestic loads and the other integrating EV loads alongside household demand. Using ToU pricing, it determines the optimal charging and discharging strategies for the SPBESS, evaluates their implications for the system configuration and grid ToU pricing, and quantifies the annual reductions in power purchases, electricity bills, energy costs, and carbon emissions. Moreover, the study comprehensively analyzes the optimal power exchanges between the photovoltaic system, battery energy storage system, and the grid, precisely considering the specific load requirements and grid ToU pricing. The conducted research findings manifest considerable decreases in energy costs, with the domestic load condition witnessing a reduction from $0.312 per kilowatt-hour to $0.245 per kilowatt-hour and the scenario involving EV and residential loads experiencing a decline from $0.27 per kilowatt-hour to $0.197 per kilowatt-hour. Furthermore, the annualized cost savings for cases 1 and 2 amount to $1,336 and $4,544, respectively, yielding substantial reductions in polluting gas emissions. Besides, the IoT-based constructed prototype model design for real-time power parameters monitoring and control shows the importance of IoT-based monitoring for remote load control, allowing users to maximize energy efficiency, resource utilization, and system visualization.

Investigation of self-excited induction generator for supporting domestic loads and its extension to a microgrid

This study provides a technical and financial analysis for the incorporation of a microgrid structure with a wind energy conversion system for producing electricity. This study’s primary aim is to provide solutions for issues that arise when isolated induction generators are employed with microgrids. A closed-loop smart electronic load controller is used to regulate the loads in the system that are supplied by the generator. A switched variable capacitor bank is used to supply reactive power initially during a voltage dip at varying winds and loads to sustain the voltage profile. Additionally, a simple voltage control loop-based controller for the generator-side converter maintains the voltage at a steady level. Using HOMER software, an economic study of the suggested wind-based microgrid structure is also presented. A laboratory experimental setup is used to support the MATLAB/Simulink study of the proposed method and its control. The findings support the feasibility of implementing the suggested plan in grid-isolated regions for supplying critical loads.

MODELING OF A STANDALONE SOLAR PHOTOVOLATIC (PV) GENERATION SYSTEM

This brief discusses the Modelling of a Standalone solar Photovolatic (PV) generation system. In this work the simulation of the PV system in open loop mode is carried out in MATLAB/Simulink environment and the real time hardware implementation has also been done. The PV panel output is fed to an inverter which supplies power to domestic load such as fans, lights. A battery and charge controller are also used in the system. The two results have been compared. The simulation and hardware results have been found matching with each other. Key Words: PV system, OFF-grid, Batteries, Inverter and Load.

Techno-economic analysis of off-grid hybrid wind-photovoltaic-battery power system by analyzing different batteries for the industrial plant in Shiraz Industrial Town, Iran.

The world has moved toward renewable energy resources for three major reasons: (1) to mitigate climate change arising from the excessive emission of greenhouse gases, (2) to protect health by lowering greenhouse gas emissions, and (3) to meet ever-increasing demands for energy. Shiraz is a major city in Iran and struggles with pollution challenges due to the presence of highly polluting industries. The increased energy demand and the lack of a demand-supply trade-off have led to frequent power outages in Shiraz in recent years. Batteries have been of great interest to researchers as they have a wide range of compounds and variety in the market and strongly influence the function and initial costs of hybrid energy systems. This study models a hybrid renewable energy system using four different batteries, that is, lead-acid, Li-ion, vanadium redox, and zinc-bromine batteries. These four scenarios were subjected to techno-economic analysis in HOMER. The system was assumed to generate 3000 kW of industrial power and 300 kWh of office/domestic power. It was demonstrated that the hybrid system with the lead-acid battery was the most optimal system to supply power to the case-study industrial plant for both industrial and domestic load, with a levelized cost of energy of 0.47 USD/kWh and an initial cost of 6.02 million USD. However, the hybrid system with the Li-ion battery will become more optimal than the system with the lead-acid battery if Li-ion batteries continue to become more affordable in < 5 years. This system would decrease CO2 emissions by 1,060,133 kg every year as compared to the diesel system.

Designing an optimal hybrid microgrid system using a leader artificial rabbits optimization algorithm for domestic load in Guelmim city, Morocco

This research endeavor strives to conceptualize a hybrid microgrid tailored for household consumption in Guelmim City, situated in the southern region of Morocco. In pursuit of this objective, a groundbreaking and enhanced algorithm, denoted as the Leader Artificial Rabbits Optimization Algorithm, is introduced. The efficacy of this innovative algorithm is initially validated by means of an exhaustive performance assessment involving a comprehensive comparison with the original version of the algorithm as well as other contemporary optimization methodologies. This comparative analysis encompasses the evaluation of the algorithms across 23 benchmark functions. The outcomes clearly exhibit that the developed algorithm outperforms both the original iteration and other optimization techniques in terms of both convergence speed and solution quality. Subsequent to this validation, the proposed algorithm is applied to the specific project at hand. The resulting outcomes are juxtaposed with those derived from other optimization strategies. This comparative evaluation serves to underscore the effectiveness of the suggested approach. The outcome of this comparison highlights a net present cost of $106,821, based on the microgrid configuration employing PV/wind/diesel/battery. Additionally, the cost of energy is estimated at approximately $0.11 per kWh, accompanied by a reliability rate of 5 % and an availability rate of 95 %.

Analysis of 38 kW class agricultural tractor engine load rate according to agricultural operation

This study was conducted to analyze the engine load ratio of a 38 kW agricultural tractor to calculate national emissions. Engine output was calculated using engine CAN (Controller area network) data measured through a load measurement system. In this study, plow tillage (PT) and rotary tillage (RT), which are the main agricultural tasks of small and medium horsepower tractors, were selected and performed three times for each stage. The engine load ratio was calculated as the ratio of the engine power generated during work and the engine rated power, and weights (25% each for PT M1, PT M2, RT L3, and RT L4) according to the agricultural work ratio were applied. The engine load ratio was found to be an average of 0.68 and 0.75 for the M1 and M2 stages of plow tillage work, respectively, and an average of 0.57 and 0.65 for the L3 and L4 stages of rotary tillage work, respectively. The weighted load ratio was 0.17, 0.19, 0.14, and 0.16 for plow tillage operation M1, M2, and rotary tillage operation L3 and L4, respectively. The integrated load factor was found to be 0.66, which is approximately 1.38 higher than the current domestic load factor of 0.48.

Enhancing sustainable urban economies: A statistical investigation of transitioning from fossil fuel-dependent industries to green innovation hubs

As a result of the evolution of the smart grid, the new energy market has been restructured in urban systems, whereby domestic users play a more active role in the demand response (DR) programs with a view to balancing production and demand. DR incentive signals, though, are difficult to respond to due to limited understanding by users. As a result, an Energy Management Controller (EMC) has been expanded for responding to DR signals and solving energy management problems. The EMC employs a whale optimization algorithm (WOA) for automatically responding to the DR signal and solving energy management issues by planning 3 kinds of domestic load: non-interruptible, interruptible, and hybrid. With the WOA, the EMC generates the best home load plan for reducing power costs, carbon emissions, PARs, and customer dissatisfaction. Simulations were accomplished in order to evaluate the offered WOA against current algorithms such as particle swarm optimization (PSO), differential evaluation (DE), and teaching learn optimization algorithm (TLBO). The outcomes indicated that the suggested WOA reduces electricity costs, carbon emissions, PARs, and consumer dissatisfaction better than other algorithms.

Justifying calculations of snow load distribution on snow retaining structures in avalanche protection system

Snow load estimation is one of the most important tasks in designing snow-retaining structures for avalanche protection of railways and motorways in mountainous areas. The situation is complicated by the fact that avalanches are subdivided, depending on the consistency of snow, into dry, wet and damp avalanches. This paper analyses the existing foreign and domestic snow load calculation methods, based on which was concluded that there is no dependence of the load value on the physical and mechanical parameters distribution of the snow cover in all the considered methods of calculation. Moreover, none of these methods makes it possible to estimate the snow pressure distribution on a snow-retaining structure by its depth. The article proposes an alternative way of calculating the snow layer pressure using the developed discrete model for calculating the static snow pressure distribution on a barrier by its depth. The discrete model peculiarity is to take into account the change of physical and mechanical characteristics of snow — density, specific adhesion and angle of internal friction, by layers of natural deposit. Discrete model verification was performed by comparing the calculated and experimental data obtained as a result of both natural and laboratory experiments. The experiments were conducted in laboratory conditions on a special model, using a material with stable physical and mechanical characteristics that do not change during the course of the experiment. Also, for verification, the field experiment results on determining the naturally deposited snow pressure on an obstacle, taken from the open press, were used.

Predictive analysis of Low power DC loads in Residential Buildings

There has been a constant rise in use of loads powered from direct current (DC) among the residential loads. In the traditional Alternating Current (AC) grid voltage undergoes conversion multiple times before it reaches the loads requiring DC, which leads to substantial power loss in the converters. Implementation of a DC grid in the residential buildings can reduce the losses caused due to the conversions. The paper presents a prediction model for domestic low power DC loads which can be powered from a battery. The data of usage of the load as well as the data of the parameters on which the load usage is dependent on was collected over a period of a year. This data was fed to a Random forest-based machine learning algorithm to predict the usage of the loads in future. The state of charge (SOC) of the battery used to power the loads was compared with the load prediction to decide whether the load can be powered from the available power in the battery or not. This allowed the system to decide in real time on the source to be used to power the DC loads and providing the users a choice to change the load usage time in order to prevent power from being drawn from the AC grid.

Bidding strategy of the virtual power plant consisting of thermal loads controlled by thermostats for providing primary frequency control ancillary service

The purpose of this article is to provide a model for the effective use of on/off loads and thermal loads controlled by thermostats (TCL) in providing primary frequency control (PFC). To this end, the mentioned loads have been aggregated in the form of a virtual power plant (VPP), allowing their cumulative capability to be offered as a bid for ancillary services. In order to determine VPP bids, a droop curve similar to the droop curve of traditional power plants has been extracted for VPP, which includes the technical limitations of VPP member loads. In formulating the VPP bidding strategy, the goal is to maximize profits and to use the capabilities of TCLs more effectively; their temperature set point is considered variable, as well as the constraint of compressor lockout, energy consumption level, device’s fire frequency and the duration of their continued response (deployment end) are incorporated in the model. The results of the model are the maximum power that can be provided to the primary frequency ancillary service market and its profit. The presented model has been simulated on a test system including 70 domestic loads in MATLAB software and checked under five different scenarios. According to the obtained results, considering the variable temperature set point for TCL leads to increasing the power of VPP by 7.33%. Moreover, considering compressor lockout constraint and increasing deployment end lead to a 22.4% and 3.54% reduction to the power of VPP respectively.

Community stochastic domestic electricity forecasting

The domestic sector is a significant energy consumer – accounting for around 40% of global electricity demand – due to household demand diversity and complexity. An accurate and robust estimation of domestic electrical loads, environmental impacts, and energy-efficiency potential is crucial for optimal planning and management of energy systems and applications. However, uncertainties resulting from simplistic socio-technical attributes, microclimatic variations, and oversimplification of the effects of interdependent variables make domestic energy modelling challenging. In this research, a hybrid bottom-up community energy forecasting framework is developed to estimate sub-hourly domestic electricity demand using a combination of statistical and engineering modelling approaches by considering key factors influencing household consumption, including demographic characteristics, occupancy patterns, and the features, ownership, and utilisation patterns of electric appliances. The framework is tested on a community in Wales, UK and validated on an annual, daily, and sub-hourly basis with monitored electricity usage averages derived from the UK Energy Follow-Up Survey and the sub-national electricity consumption datasets. Results closely reflect annual and daily household demand at individual dwellings and aggregated levels, with an estimation accuracy of up to 90%. Moreover, the framework facilitates more reliable sub-hourly demand profiles compared to conventional simulation practices that overestimate daily electricity demand and sub-hourly peaks by up to 15% and 50%, respectively.

A novel ANFIS-controlled customized UPQC device for power quality enhancement

Power quality is crucial for the reliable and efficient operation of domestic and industrial loads. Nonlinear power electronic converter loads have increased in usage, which has led to a decline in both voltage and current quality. To overcome these power quality issues, a universal power quality compensator (UPQC) has been developed and integrated into the power distribution network. The UPQC comprises dual active power filters which are equipped with a DC-link capacitor. The voltage of the DC-link capacitor is controlled by a DC-link voltage controller. However, the conventional PI controller is not suitable for regulating voltage at a defined level due to inappropriate gain values. In this work, we propose an intelligent adaptive neuro-fuzzy inference system-based DC voltage controller for customized UPQC devices. The proposed control scheme aims to mitigate the existing power quality challenges by reducing the issues in classical controllers and incorporating intelligence knowledge with subjective decisions. Using the MATLAB/Simulink software tool, we have tested a customized UPQC device controlled by ANFIS for critical operation and performance. The simulation results are presented along with valid comparisons.

Power Quality Impacts of Grid-Tied PV Inverters on Low Voltage Distribution Networks a Smart OpenDSS Model to Find Power Quality Threshold Limits

As a renewable initiative, risen attention can be seen in solar photovoltaic (PV) installations in the world. Since tropical countries and even many other countries during summer experience higher irradiance levels, there is great potential for PV generation in solar panels. However, power quality can be impacted when the number of PV modules is increased in a network due to the emergence of harmonics, unbalanced voltages, voltage flicker, neutral voltage variations, etc. Therefore, it is essential to empirically analyse power quality parameters, i.e., total harmonic distortion (THD), voltage unbalance, etc., and find threshold margins of PV capacities in a network. Based on a case study conducted in Negombo, Sri Lanka, this work determines power quality impacts with reference to the number of PV interconnections in a distributed network. For the task, a low-voltage distribution network was chosen which was fed by a 250kVA, 11/0.4kV transformer with domestic loads and grid-tied PV inverters. A simulation model was developed in Open Distribution System Simulator (OpenDSS) with time-varying load patterns and PV generation. Consequently, a smart efficient method was introduced to model domestic loads with unique time-varying demand patterns. A determination criterion was established to derive snapshot load flow instants using time-varying load flow results to analyse voltage profiles along distribution feeders. Then, the model was enhanced to quantify the power quality parameters such as individual harmonic content, THD of voltage as well as current and neutral voltage variation. The results reveal that node voltage has improved with PV interception without violating the upper limit. The THD of voltage and current have slightly increased with the addition of PV inverters.

Maximising the benefit of variable speed heat-pump water heater with rooftop PV and intelligent battery charging

Domestic applications of thermal energy storage, interacting with rooftop PV and batteries can substantially reduce consumer electricity costs, whilst assisting grid stability. Air source heat pump water heaters (HPs) with inherent thermal storage offer significant purchased energy savings, however, their typical control only considers stored water temperature. We demonstrate that further energy and cost reductions are possible using variable compressor speed controls that optimise thermal performance in real-time, whilst coordinating energy consumption to match available PV or shifting consumption to off-peak hours. The incremental complexity of variable-speed HP water heaters can reduce HP electricity consumption from the grid by up to 28% without PV or battery, 88% with PV, and 99% using PV and battery storage. By integrating the HP and all other domestic electrical loads, we exploit the combination of thermal and battery energy storage using rules-based and machine-learnt hierarchical controls to reduce the net household energy cost. Machine-learnt grid battery charging is shown to reduce annual grid energy consumption by 84%, delivering 99% of the potential energy cost savings. We conclude that a HP with inherent thermal storage, interacting with rooftop PV offers the most cost-effective home energy storage solution, lowering net lifetime costs by up to 27%.

Rotating Electrical Machines: Types, Applications and Recent Advances

The Rotating Electrical Machines (REMs) are classified into Motors and Generators. They powered the industrial, domestic and commercial loads. Because of their importance. This paper discussed different types of REMs, their applications and recent advances. REMs are applied in Teaching, Domestic, Mechatronics, Motorcycle, Three-wheelers, Electric Vehicle, Healthcare, Flywheel Energy Storage and Wind Energy Conversion Systems. It periscopes the advances of REMs in design, Fault diagnostic, control and condition monitoring. Its significance is to shed light on some advances made in REM.