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This paper presents a sensorless control designed for low to medium speed operation of an 8/6 pole switched reluctance motor (SRM) drive, utilizing only a single current and voltage sensor while ensuring grid-side power quality. It emphasizes a cost-effective solution suitable for low-power applications in both domestic and industrial appliances, employing SRM drive technology. In this, AC input is fed to Cuk converter that suppresses grid current total harmonic distortion (THD). Using symmetrical structural properties of SRM allows for a cost-friendly sensorless control approach. Position estimation for four-phase SRM is achieved by comparing two-position reference fluxes with running flux. In case of four-phase SRM, phases A and C are conducted sequentially, enabling a single voltage and current sensor to estimate flux for both phases. This method contrasts with lookup table-based rotor position estimation, requiring only two sets of position flux data, thus reducing memory usage, controller complexity, and speeding up response. A laboratory prototype of a four-phase 8/6 SRM is used to estimate rotor position in sensorless control. Finally, test results are used to validate effectiveness of presented PFC assisted sensorless control approach.

Population expansion and improving living standards, particularly in developed nations, have led to an increase in the usage of domestic electrical equipment, worldwide energy consumption, and CO2 emissions per capita. To limit the usage of non-reusable components and the amount of garbage that must be transferred at the end of a product's life cycle, longer-lasting electrical domestic appliances are a pillar of the circular economy. In recent years, the complexity of printed circuit boards (PCBs) used in the manufacture of modern electrical devices has increased, leading to an increase in device failures. This study focuses on the maintenance and recycling of domestic electrical appliance components and printed circuit boards. The proposed methodology for PCB repair is defined as a sequential quadratic programming (SQP) problem implemented in MATLAB environment and successfully tested to a variety of domestic appliances such as refrigerator, dishwasher and washing machine. The possibility of recycling metal parts of electronic components, which were replaced after PCBs' repair was also studied. Metals' percentage concentration of PCB electronic components for three customer's budgets considering metals and valuable metals recovery as given from the corresponding average metal recovery and calculated from different recycling procedures presented in the literature. The results of the proposed procedure in terms of valuable metals gave 38.4078 ppm of silver. We also compared the suggested procedure with other works in terms of environmental perspective considering four measures, namely the gross energy requirement (GER), the global warming potential (GWP), the acidification potential (AP), and the solid waste burden (SWB). In terms of economic perspective and considering the existence of silver (Ag) in the electronic components, the recommended method gave comparable amount of money. Finally, a comparison of different recycling works from a technical viewpoint is also conducted. Moreover, a reparability index of domestic electrical appliances is introduced to further quantify the results of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Induction heating (IH) is the most preferred technology for domestic and industrial heating applications owing to its advantages over conventional heating methods. Heating requirements for ferromagnetic (FM) and non-ferromagnetic (N-FM) materials are different. There is an increase in research for heating these different material vessels. In this research work, a three switch dual frequency resonant inverter (3 S-DFRI) with dual frequency control is proposed. It has been used for simultaneous and independent heating of steel and aluminum loads. The inverter operation is verified by rigorous simulation and experimentation on the developed 2 kW prototype. It can also be extended for multiple loads by additional inverter legs each with three switches. Each additional leg provides two independent load operation. Compatibility with different material IH loads, reduced number of switching devices, no use of electro-mechanical switches, compact size and high efficiency are the key advantages of the proposed inverter. [ABSTRACT FROM AUTHOR]

The research discusses how to design the multi-input electricity extension plug board, known locally as the electrical connection panel, which is one of the most important products or simple tools that people rely on in their daily lives, and it is indispensable in connecting electrical-powered device. However, sometimes these products may be the most dangerous If its manufacturing’s quality is neglected or misused especially these types of products are found in all homes, companies, and institutions. The research aims to redesign the conventional electrical plug-in board based on identifying the deficiencies of the traditional product common among users.The study made it possible to identify the potential risks that occur because of faulty design and poor product materials, thereby re-designing an adjustable multi-entrance electricity extension plug board that takes into account the proposed solutions for a safe and effective design. The researchers also provide different design solutions that suit the interaction environment and periodic maintenance of the product, which makes it suitable for the process of human use and promotes the concept of usability of products which makes the work environment less stressful and more enjoyable.

Abstract A primary task of Non‐intrusive Load Monitoring (NILM) is the identification of appliances that are switched on or off. However, state‐of‐the‐art machine learning methods such as deep learning do not express uncertainty of their predictions. Especially in cases where appliances are confused, it is desirable that an NILM system can suggest multiple possible predictions to the end‐user, including its confidence and credibility of any given prediction. This can be achieved using conformal prediction, being an effective way to quantify uncertainty of a given machine learning model. In this work, conformal prediction is introduced for NILM and applied to a neural network. The approach is explained and supported by several examples.

A primary task of Non‐intrusive Load Monitoring (NILM) is the identification of appliances that are switched on or off. However, state‐of‐the‐art machine learning methods such as deep learning do not express uncertainty of their predictions. Especially in cases where appliances are confused, it is desirable that an NILM system can suggest multiple possible predictions to the end‐user, including its confidence and credibility of any given prediction. This can be achieved using conformal prediction, being an effective way to quantify uncertainty of a given machine learning model. In this work, conformal prediction is introduced for NILM and applied to a neural network. The approach is explained and supported by several examples. [ABSTRACT FROM AUTHOR]

Abstract International policies and targets to globally reduce carbon dioxide emissions have contributed to increasing penetration of distributed energy resources (DER) in low‐voltage distribution networks. The growth of technologies such as rooftop photovoltaic (PV) systems and electric vehicles (EV) has, to date, not been rigorously monitored and record keeping is deficient. Non‐intrusive load monitoring (NILM) methods contribute to the effective integration of clean technologies within existing distribution networks. In this study, a novel NILM method is developed for the identification of DER electrical signatures from smart meter net‐demand data. Electrical profiles of EV and PV systems are allocated within aggregated measurements including conventional electrical appliances. Data from several households in the United States are used to train and test classification and regression models. The usage of conventional machine learning techniques provides the proposed algorithm with fast processing times and low system complexity, key factors needed to differentiate highly variable DER power profiles from other loads. The results confirm the effectiveness of the proposed methodology to individually classify DER with performance metrics of 96% for EV and 99% for PV. This demonstrates the potential of the proposed method as an embedded function of smart meters to increase observability in distribution networks.

Abstract With the development of smart grids, appliance‐level data information plays a vital role in smart power consumption. Nowadays, appliance signatures detected by non‐intrusive load monitoring (NILM) can be used for anomaly detection, demand response, and electricity management. These applications increase the requirements for the accuracy of appliance identification in NILM. And it has been proved that the voltage–current (V–I) trajectory can be applied as an effective load signature to represent the electrical characteristics of appliances with different statuses in previous researches. In this paper, a V–I trajectory enabled asymmetric deep supervised hashing (ADSH) method has been proposed for NILM. ADSH method converts load identification into the large‐scale approximate nearest neighbour search. Different from the existing methods, ADSH treats the query images and database images in an asymmetric way in order to improve accuracy. More specifically, ADSH learns a deep hash function only for query images, while the hash codes for database images are directly learned. The experimental results on REDD and PLAID datasets show that the proposed method significantly improves the accuracy of load identification compared with state‐of‐art methods.

Abstract The deployment of domestic smart metering infrastructure in Great Britain provides the opportunity for identification of home appliances utilising non‐intrusive load monitoring methods. Identifying the energy consumption of certain home appliances generates useful insights for the energy suppliers and for other bodies with a vested interest in energy consumption. Consequently, the domestic smart metering system, which is an integral part of the smart cities' infrastructure, can also be used for home appliance identification purposes taking into account the limitations of the system. In this article, a step‐by‐step description on accessing data directly from the domestic Smart Meter via an external Consumer Access Device is described, as well as an easy‐to‐implement method for identifying commonly used home appliances through their power consumption signals sampled at a rate similar to the rate available by the domestic smart metering system. The experimental results indicate that the combination of time domain with frequency domain features extracted either from the 1D/2D Discrete Fourier Transform or the Discrete Cosine Transform provides improved recognition performance compared to the case where the time domain or the frequency domain features are used separately.

Abstract The performance of non‐intrusive load monitoring (NILM) systems heavily depends on the uniqueness of the load signature extracted from the electrical appliances. Different load signatures have been proposed. Recently, in particular, v–i trajectory feature extraction is attracting more and more attention due to its unique characteristics. Herein, instantaneous p–q load signature (IpqLS) feature extraction is first proposed and applied in NILM, which shows that conventional methods cannot distinguish load signatures under some situations. Applying IpqLS with several machine learning algorithms is not only extracting unique features compared to the overlapping problems of P–Q and v–i trajectory but also improving load classification accuracy. Simulations and experimental results verified the effectiveness of the proposed method.

In load modelling studies, the behaviour and electrical characteristic of a load are the main entries used in the identification of that load. This study is focused on the electrical characteristics of light bulbs (compact fluorescent light, fluorescent, incandescent, and light‐emitting diode) with different characteristics that can be found in a house. While discovering these characteristics, current waveforms were used and electrical features were revealed. In addition, a novel method is proposed to identify individual loads by using these features. In this scope, a novel similarity score measure is introduced. In the presented study, similarity score measure is calculated from the total harmonic distortion, power, and harmonic current features. The experiments on randomly selected loads demonstrate that the proposed method successfully identified the loads even if they have similar characteristics or power consumption. [ABSTRACT FROM AUTHOR]

The author argues that resistance to new technology does not result from “technophobia” but rather from the fact that the new technological object is inscribed with new meanings and frameworks of action that may disturb the existing networks of practices, beliefs, and knowledge. Based on an analysis of 24 dyadic in-depth interviews and 52 individual in-depth interviews with people aged 47–91 living in Warsaw and Poznan (Poland), five sources of resistance to new domestic technologies were identified: (1) disruption of “flow” (Csikszentmihalyi 1990) – the pleasure resulting from a habitual and automatic action, the synchronization between the body and its material surroundings; (2) disruption of the “natural order of things” (the “mental map,” Kaufmann 2001) – a sense of security, durability or safety resulting from the stability of the spatial and material arrangement of the home (a sense that everything is in its place); (3) differences of standards and cultural ideals regarding acceptable practices, the quality of their performance, and their effect; (4) possible disturbance in relationships between members of the household; (5) beliefs about what is right, moral, or correct, as well as dissonances between different pieces of knowledge and beliefs, and between an opinion or belief and an embodied practice.

Load shedding (LS) is implemented by distribution utilities (DUs) in addressing power supply insufficiency problems to avoid DU system damages. This is commonly implemented by the installation of LS relays in every DU feeder. However, in either scheduled or unscheduled supply disruptions, a huge amount of unnecessary de-loading is taking place in a feeder level LS implementation. In addition, the consumers connected to a de-loaded feeder are in total blackout, that is, consumers have no choice over which appliances to spare from being de-loaded. This study proposes an LS implementation that replaces feeder-level de-loading by a finer consumer-appliance-level de-loading and allows consumers to have some control over their de-loading. In this method, consumers can set an appliance priority level to their selected connected loads at a given time, to avoid a total blackout. Furthermore, to deal with the enormous data involved in this proposed method, both centralised and distributed optimisation approaches are employed to expedite the system processing response. Simulations are conducted to verify the proposed method's functionality. Lastly, economic analysis is done to assess the proposed method's viability.

Simple analysis of energy consumption patterns recorded by smart meters can be used to deduce household occupancy. With access to higher-resolution smart-meter readings, we can infer more detailed information about the household including the use of individual electric appliances through non-intrusive load monitoring techniques. The extent of privacy concerns caused by smart meters has proved to an obstacle in the roll-out of smart meters in some countries. This highlights the need for investigating smart-meter privacy. Mechanisms for ensuring smart-meter privacy fall in broad categories of data manipulation, demand shaping, and load scheduling. In smart-meter data manipulation, the smart meter collects real, potentially high-resolution data about the energy consumption within the house. This data is then manipulated before communication with to utility providers and retailers. The manipulation could be non-stochastic, such as aggregation, binning, and down-sampling, or stochastic, such as additive noise. In demand shaping and load scheduling, smart-meter readings are communicated without any interference but the consumption is manipulated by renewable energy sources, batteries, or shifting loads to render non-intrusive load monitoring ineffective. In this study, the author reviews these approaches and presents several methods relying on homomorphic encryption, differential privacy, information theory, and statistics for ensuring privacy.

Networked and interconnected microgrids can improve resilience of critical end-use loads during extreme events. However, the frequency deviations in microgrids during transient events are significantly larger than those typically seen in bulk transmission systems. The larger frequency deviations can cause a loss of inverter-connected assets, resulting in a loss of power to critical end-use loads. Grid Friendly Appliance^TM (GFA) controllers can mitigate the transient event effects by engaging end-use loads. This paper presents a method to select set-points for end-use loads equipped with GFA controllers, while minimizing the interruptions to end-use customers. An online (i.e. real-time), device-level algorithm adjusts individual GFA controller frequency setpoints based on the operational characteristics of each end-use load and on the changing grid dynamic characteristics to selectively engage the load for mitigating the switching transients. The adaptive gradient-descent-based algorithm does not require control or coordination amongst end-use devices for adapting frequency set-points. The method is validated using dynamic simulations on a modified version of the IEEE 123-node test system with three microgrids using the GridLAB-D^TM simulation environment. The improved dynamic stability achieved through the engagement of GFAs support the switching operations necessary for networked microgrid operations.

The ever-increasing energy demand and extreme weather days lead to more frequent power outage events. This paper proposes a hierarchical energy management scheme for residential communities, aiming to facilitate energy sharing among houses and minimize the impact on the grid outage on the whole community. In the proposed model, the complexity of scheduling residential energy resources of multiple houses is decomposed into a bi-level structure, in which the Home Energy Management System (HEMS) of each house iteratively interacts with the Community Energy Management System (CEMS). In the upper layer, the CEMS receives the information of the planned outage; it then solves a social warfare optimization model to determine: (1) charging/discharging power of a community battery energy storage system, and (2) load re-shaping instructions of each house. In the lower layer, the HEMS of each house performs appliance-level autonomous scheduling to try to satisfy the load re-shaping instructions received from the CEMS. The autonomous scheduling result of individual HEMSs are sent back to the CEMS, and the latter updates the upper-layer result based on the received result. This process continues until the convergence criteria is achieved. Extensive simulations are conducted to validate the proposed solution

This paper develops a model to characterize the demand for domestic water based on its end users' usage habits. The use of individual residential appliances (bathroom sink, toilet, shower, bath, etc.) is interpreted using a probabilistic approach. The paper also applies the model to the distribution network of the municipality of Sparanise, a small city in the province of Caserta, Italy. The results of this application are compared to the real output of the city's actual water reservoir. Flow variability during the day was successfully modelled. A comparison of the simulated and recorded data on a daily level indicates the proper adjustment of the volume distribution; the peak flow rates were also comparable. The model could be a useful tool for analyzing domestic water consumption, especially in the design and management of water distribution networks. Use of the model would particularly aid the Integrated Urban Water Management Operator both in optimizing the operating pressures in the various districts’ networks and in predicting domestic water consumption when drafting its water balance documents.

The design of longer-lasting products, such as domestic electric appliances, is a key-stone approach of the circular economy to reduce the use of non-reusable materials and the number of wastes to be managed at the end of the product’s life as well as to extend it. The manufacturing of modern electric appliances includes the incorporation of printed circuit boards (PCBs). PCBs provide mechanical support and electrically connect electrical or electronic components using conductive trackpads and other features etched from one or more sheet layers of copper laminated onto and/or between sheet layers of a non-conductive substrate. This paper proposes a PCB maintenance framework, fully compliant with the “Right to Repair” concept, considering the impact of their aging failures based on measurements made on them, as well as the repair and replacement costs of their components. Herein, we present an algorithm that assesses the problem of handling the repair and replacement cost corresponding to specific failures while ensuring that the total cost of repair does not exceed a predefined value. This is achieved through an integer linear programming (ILP) formulation which maximizes the benefit to the life expectancy, Li, of an appliance, constrained by a customer’s limited budget. The proposed methodology is tested with different PCBs and considers different types of appliances. More specifically, two cases concerning PCBs of washing and dishwasher machines are studied to examine the dependency of the solutions on the aging rate of their various components. The simulation results show that considering a medium budget, after 3 years, we can achieve a health benefit of 92.4% for a washing machine’s PCB, while for a dishwasher’s PCB, the health benefit drops to 86.3%.

A voltage-controlled canonical switching cell-derived DC–DC converter with improved power quality feeding a variable speed permanent magnet brushless direct current (PMBLDC) motor-drive system for home appliances, is presented in this study. The converter is operating in the continuous conduction mode (CCM). This converter has less stress on the converter devices due to CCM of operation. A single converter is used for both the power factor correction (PFC) and the speed control of the drive, thus reducing the number of elements in the system. The speed control, along with PFC in this system is achieved via a PI controller having speed reference as an input. This system is capable of providing the low speed to rated speed operation while maintaining a unity power factor. The Hall-effect sensor-based electronic commutation of a PMBLDC motor-drive is implemented at the fundamental frequency. This significantly improves efficiency by reducing the switching losses of the voltage source inverter. A prototype of it is developed in the laboratory for implementation of the scheme. Simulated and real-time experimental results of the scheme are presented here for performance validation at various dynamic and steady-state operating conditions. [ABSTRACT FROM AUTHOR]

Residential load scheduling has been introduced in recent years as a result of implementing demand response programmes in the residential sector. Up to now, studies have focussed on developing algorithms for scheduling the residential loads with the objective of reducing the electricity payment while satisfying the users' comfort level. To aim at this goal, the user should determine the desired scheduling window for each appliance to initiate the scheduling process. Although these algorithms designed to benefit the end‐users, determining the scheduling window for each appliance at the beginning of the scheduling process can be challenging. In this study, an intelligent load scheduling model is proposed to perform the scheduling with the least user intervention. The proposed model predicts the preferred scheduling window for each appliance and initiates the scheduling process. Furthermore, the degradation cost of the battery pack in the vehicle‐to‐grid mode is considered in the cost function. The model is applied to a case study containing plug‐in hybrid electric vehicle and flexible loads such as a washing machine and dishwasher under inclining block rates and hourly pricing tariffs. The results indicate that implementing the model in a residential building can decrease the electricity payment while performing the residential load scheduling with the least user intervention. [ABSTRACT FROM AUTHOR]

Load shedding (LS) is implemented by distribution utilities (DUs) in addressing power supply insufficiency problems to avoid DU system damages. This is commonly implemented by the installation of LS relays in every DU feeder. However, in either scheduled or unscheduled supply disruptions, a huge amount of unnecessary de-loading is taking place in a feeder level LS implementation. In addition, the consumers connected to a de-loaded feeder are in total blackout, that is, consumers have no choice over which appliances to spare from being de-loaded. This study proposes an LS implementation that replaces feeder-level de-loading by a finer consumer-appliance-level de-loading and allows consumers to have some control over their de-loading. In this method, consumers can set an appliance priority level to their selected connected loads at a given time, to avoid a total blackout. Furthermore, to deal with the enormous data involved in this proposed method, both centralised and distributed optimisation approaches are employed to expedite the system processing response. Simulations are conducted to verify the proposed method's functionality. Lastly, economic analysis is done to assess the proposed method's viability. [ABSTRACT FROM AUTHOR]

Induction heating is regarded as a clean cooking technology, whose prominent advantages include contactless energy transfer, controllable heating rate, and safety. In household applications, it is expected that this approach is supposed to replace conventional gas stoves aiming at a more sustainable future. The increase of energy consumption associated with this trend cannot be neglected, while distributed generation (DG) stands out as a prominent solution to mitigate the impact on the power system. In this context, this work presents an induction heating system consisting of the integration of power electronic converters and a grid‐connected photovoltaic (PV) system. Based on existing solutions available in the literature, it is possible to supply the induction stove with two distinct energy sources: the ac grid and PV modules. A high‐voltage step‐up dc–dc boost converter is employed to create a dc link responsible for connecting the PV system to the grid. The dc link is also used to supply a class DE parallel resonant inverter associated with the induction stove, which requires a low component count. Besides, the PV modules are capable of providing up to 3 kW under standard test conditions. Thus, part of the generated energy is used to supply the heating system, while the remaining portion is injected into the grid. Simulation results are presented to assess the performance of the whole system in steady‐state and transient conditions, demonstrating that it is capable of operating at the maximum power point and injecting nearly sinusoidal currents with high power factor. [ABSTRACT FROM AUTHOR]

As plentiful roof‐top photovoltaic (PV) panels and battery energy storage systems (BESSs) have been installed in residential houses, smart homes which provide reliable and cost‐efficient energy for customers have been increasingly developing. This study proposes a smart home coordinated operation framework consisting of day‐ahead scheduling of a BESS and deferrable appliances as well as real‐time BESS affinely adjustable control. The scheduling aims to minimise daily energy usage bills and the affinely adjustable control aims to eliminate fluctuations of intermittent PV output and at‐will uncertain appliances. This study proposes an affinely adjustable robust energy management system (AAREMS) which can optimise the day‐ahead scheduling with full consideration of the real‐time BESS control under uncertainties. Moreover, affinely adjustable robust counterpart formulation is developed to solve the AAREMS under the worst case of real‐time uncertainty realisation. The simulation results verify the high efficiency of the proposed AAREMS in minimising the energy usage bills for the customers and providing certain and invariable power injection for the utility grid. [ABSTRACT FROM AUTHOR]

In today's market, electric bikes (e‐bikes) are becoming exceedingly popular due to their smaller size, ease of domestic charging and cost‐friendly alternative in the category of electric vehicles. So, there is a need for the development of a power factor corrected (PFC) private off‐board charger (OBC) to cater to the need of the domestic charging of e‐bikes. Traditionally, e‐bike chargers powered by a domestic meter are realised by a non‐PFC OBC with a power rating of 500–700 W. Such converters draw harmonic currents at very high crest factor and affect the life and performance of other domestic appliances. A private OBC which derives its power from the domestic supply for charging the battery of an e‐bike is proposed. The charger has two stages, i.e. the PFC stage for input current shaping and the DC–DC stage for regulating the battery charging voltage and charging current. The PFC stage is based on the single‐ended primary inductance converter and the DC–DC stage is based on the inductor inductor capacitor (LLC) resonant converter. A 570 W, 57.6V, 10 A prototype is built using digital signal processor (TMS320F28035) to evaluate and validate its performance for charging a lithium‐ion battery pack. [ABSTRACT FROM AUTHOR]

This paper proposes a layered stochastic optimization approach for residential demand response (DR) under real‐time pricing (RTP) and an incentive‐based mechanism, which contains three steps. In the first layer, an independent system operator (ISO) announces day‐ahead RTP to a residential load aggregator (RLA). The RLA predicts individual household loads (step 1) and aggregates the loads to minimize electrical cost (step 2). In the second layer, the RLA announces incentives to homes, and home energy management systems (EMS) control the loads to maximize the reward in real‐time (step 3). In Step 1, probability based individual load prediction models are developed. In Step 2, a stochastic optimization model is developed to aggregate controllable loads of residential consumers. In Step 3, an incentive‐based mechanism is proposed, based on which, a real‐time load control model for individual homes is developed to benefit the RLA and homeowners. A highly efficient real‐time control algorithm for home EMS is developed. The case studies show that, with 10% controllable energy integration, the peak demand is reduced by 17.5% and the energy cost of the controllable loads is reduced by 28%. The proposed mechanism can effectively aggregate many individual residential controllable loads to participate in an electricity market. [ABSTRACT FROM AUTHOR]

Proliferation of photovoltaic (PV) panels may cause voltage increases in low voltage (LV) distribution feeders. This issue confines PV hosting capacity of the feeders. To overcome this issue and enable higher PV hosting capacity, a model to manage the operation cycles of the domestic electric water heater and heating, ventilation, and air conditioning loads in order to avoid the voltage rise, thereby increasing PV hosting capacity in LV feeders is presented in this study. The model respects users' thermal comfort by setting the associated temperatures within allowed ranges. To preserve customers' privacy, the presented model is then decomposed into a master problem and several sub‐problems using the Dantzig–Wolfe decomposition algorithm. Thereafter, an effective scheme is designed to solve the decomposed problem in a distributed fashion. In the scheme, the master problem is solved by the system‐wide module and the sub‐problems are solved by house‐wide modules. The problems are all in linear format which can be easily solved with affordable computation. Importantly, the proposed model can reach the optimal solution while minimal data is shared between houses and control centre. [ABSTRACT FROM AUTHOR]

The food industry is shifting toward automated and customized processes, leading to the emergence of smart cooking devices that improve cooking outcomes. However, these devices can be invasive, costly, and only applicable to certain foods. To address these issues, a noninvasive digital twin that monitors food during cooking using a common frying pan with a temperature sensor and a weighing scale is proposed. A case study for a French crêpe is presented, in which we developed a digital twin using a neural network trained on over 400 , 000 simulation data points. The results show that the digital twin can accurately estimate the properties of the crêpe during cooking in real time with a mean absolute percentage error of less than 5% and predict when it will be cooked according to user criteria. The approach offers significant benefits over existing smart cooking devices, as it can be applied to a wide range of cooking processes. The proposed technology enables food process automation and has potential applications in both home and professional kitchens. • A digital twin based on computational simulations was developed for the realtime monitoring and predicting of food cooking. • The model database generated for training considered the two-sided French crêpe cooking process on domestic cookware. • Under certain settings, outcomes of the proposed technique were below a 5 % error. [ABSTRACT FROM AUTHOR]

New ecological trends and changes in consumer behavior are known to favor biofilm formation in household appliances, increasing the need for new antimicrobial materials and surfaces. Their development requires laboratory-cultivated biofilms, or biofilm model systems (BMS), which allow for accelerated growth and offer better understanding of the underlying formation mechanisms. Here, we identified bacterial strains in wildtype biofilms from a variety of materials from domestic appliances using matrix-assisted laser desorption/ionization-time of flight mass spectroscopy (MALDI-TOF-MS). Staphylococci and pseudomonads were identified by MALDI-TOF-MS as the main genera in the habitats and were analyzed for biofilm formation using various in vitro methods. Standard quantitative biofilm assays were combined with scanning electron microscopy (SEM) to characterize biofilm formation. While Pseudomonas putida, a published lead germ, was not identified in any of the collected samples, Pseudomonas aeruginosa was found to be the most dominant biofilm producer. Water-born Pseudomonads were dominantly found in compartments with water contact only, such as in detergent compartment and detergent enemata. Furthermore, materials in contact with the washing load are predominantly colonized with bacteria from the human.

Energy storage and demand response (DR) resources, in combination with intermittent renewable generation, are expected to provide domestic customers with the ability to reducing their electricity consumption. This study highlights the role that an intelligent battery control, in combination with solar generation, could play to increase renewable uptake while reducing customers' electricity bills without intruding on people's daily life. The optimal performance of a home energy management system (HEMS) is investigated through a range of interventions, leading to different levels of customer weariness and consumption patterns. Thus, the DR is applied with efficient and specific control of domestic appliances through load shifting and curtailment. Regarding the uncertainty associated with the photovoltaic generation, a chance‐constrained (CC) optimal scheduling is considered subject to the operation constraints from each power component in the HEMS. By applying distributionally robust optimisation, the ambiguity set is accurately built for this distributionally robust CC (DRCC) problem without the need for any probability distribution associated with uncertainty. Based on the greatly altered consumption profiles in this study, the proposed DRCC‐HEMS is proven to be optimally effective and computationally efficient while considering uncertainty. [ABSTRACT FROM AUTHOR]

The increased frequency of power outages due to natural disasters in recent years has highlighted the urgency of enhancing distribution grid resilience. The effective distribution service restoration (DSR) is an important measure for a resilient distribution grid. In this work, the authors demonstrate that DSR can be significantly improved by leveraging the flexibility provided by the inclusion of demand response (DR). The authors propose a framework for this by considering integrated control of household‐level flexible appliances to vary the load demand at the distribution‐grid level to improve DSR. The overall framework of the proposed system is modelled as a three‐step method considering three optimization problems to (i) calculate feasible controllable aggregated load range for each bus, (ii) determine candidate buses to perform DR and their target load demand, and (iii) maintain the load level in each house through home energy management during the DSR, considering uncertainties in load and solar generation sequentially. The optimization problems are formulated as linear programming, mixed‐integer linear programming, and multistage stochastic programming (solved using the stochastic dual dynamic programming) models. Case studies performed in the IEEE 123‐node test feeder show improvements in resilience in terms of energy restored compared to the restoration process without DR. [ABSTRACT FROM AUTHOR]

Population dispersion necessitates grid expansion to meet electricity demand. For many developing countries and remote communities, meeting electricity demand is a challenge due to a power generation shortage and load variability that is highly driven by weather uncertainty. Electric utilities' practical planning solutions are to disable electricity access from new residential regions, supply at least 10% of the non‐electrified regions, or follow a rotating feeder curtailment such that the new regions are electrified for a few hours daily. This study proposes an alternative framework to plan electricity access more efficiently in developing countries. A probabilistic multi‐stage optimisation framework that first incorporates in‐depth analysis of appliance operational models, second accounts for AC grid codes of operation and third anticipates consumers' actions is deployed. The framework is formulated to account for climate/weather uncertainty factors. Results show that energy efficiency can reach up to 97%, and the computation time can be improved by 99.6% with respect to the existing current state‐of‐the‐art approaches. [ABSTRACT FROM AUTHOR]

Meeting electricity demand in remote communities and non‐electrified regions in the poor developing world is a challenge. Power generation is in shortage compared to electricity demand. Electric utilities either would enforce grid's zonal load curtailment or not electrify regions. Controlling electricity demand can play a vital role in enabling electricity access; however, weather uncertainty drives electricity demand variability. This article provides an overview of current demand‐side management research, identify research gaps, and propose a more promising approach to enable electricity access. Also, it proposes manipulating appliances models to fit their operation in applications where power supply shortage is an issue. The proposed work considers the effect of the probabilistic nature of weather and meeting AC grid codes of operation. [ABSTRACT FROM AUTHOR]

With development of two‐way communication technology, residential users are able to reshape their energy consumption patterns based on demand response signals. This study proposes an optimal residential energy resource scheduling model to minimise the home electricity cost while fully considering the user's life convenience, the user's thermal comfort, and renewable uncertainties. The proposed model accounts for the characteristics of shiftable appliance, air‐conditioning system, electric vehicle's charging pattern, and renewable generation of both wind and solar power. Wasserstein distance metric and K‐medoids‐based scenario generation and reduction techniques are used to address the renewable uncertainty. An adaptive thermal comfort model is employed to estimate the user's indoor thermal comfort degree. A waiting cost model is applied to measure the user's preference on the household appliance's operation. In addition, a recently proposed metaheuristic optimisation algorithm (the natural aggregation algorithm) is used to solve the proposed model. The simulation results show the proposed model is effective in minimising the household's daily electricity bill while preserving the user's comfort level. [ABSTRACT FROM AUTHOR]

Renewable power generation combined with energy storage (ES) is expected to bring enormous economical and environmental benefits to the future smart grid. However, the ES management in smart grid is facing significant technical challenges due to the volatile nature of renewable energy sources and the buffering effect of ES units. The challenges are further complicated by the increasing size and complexity of the system, as well as the consideration of random usage patterns of electrical appliances by customers. To address these challenges, this study proposes a parallel decomposition method for large‐scale stochastic programming in a distribution system with renewable energy sources and ES units. By leveraging nested decomposition, the problem can be converted into independent sub‐problems with a series of time periods. In addition, the reformulated problem is fully parallel for speed up in execution. The performance of the proposed method is evaluated based on the IEEE 4‐bus and 33‐bus test distribution systems with real photovoltaic generation and electrical appliance usage data. The case study demonstrates that the proposed scheme can substantially reduce the system operation cost, with low computational complexity. [ABSTRACT FROM AUTHOR]

Electrical load monitoring techniques are valuable to consumer site for energy saving, permitting reduction in electricity costs. Nowadays, smart grid technology incorporates advanced load monitoring applications, enabling efficient consumption of electrical energy. Non‐intrusive load monitoring (NILM) is a moderately new practice to identify the power consumption of individual appliances of a consumer from the aggregated household at a single point of measurement. In this study, an improved NILM technique is proposed by using a shunt passive filter installed at the source side of any residential complex. The proposed method can be realised in two steps. The first step is to determine the harmonic impedance at the load side for different groups of loads for a single household. The second step is to implement a fuzzy rule‐based approach for identification of different loads at the consumer end. Suitable simulations backed by experiments are demonstrated in this study to validate the viability of the proposed methodology. [ABSTRACT FROM AUTHOR]

This work presents a cooperative demand‐side management (DSM) scenario in a low‐voltage network considering a context of liberalised electricity markets. The authors show that introducing an additional inter‐supplier cooperation mechanism among consumers enhances a better use of the flexibility consented by each individual, hence aiming at reaching a global optimum instead of optimising the costs of a few ones. To that end, a real‐time pricing (RTP) scheme is explored based on cost functions differentiated in both time and consumption level that should reflect the true energy cost. The authors apply to each consumer a commodity cost function shared among the set of cooperative users of its respective supplier as well as one common network cost function shared by all cooperative users of all suppliers in the considered network. Each individual runs, through its smart meter (SM), a decentralised optimisation algorithm defining an energy consumption schedule for a set of flexible appliances (FAs). The mechanism the authors propose ensures that a fair cost distribution between all users is achieved by reaching the Nash equilibrium. To assess their proposition, they confront it to intermediate consumption strategies through a benchmark. The results confirm that their inter‐supplier cooperation mechanism always leads to the minimum total cost. [ABSTRACT FROM AUTHOR]

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By effectively adjusting the appliance usage patterns of customers, demand response (DR) is expected to bring significant economic and environmental benefits to the future smart grid. Two kinds of appliances should be considered for DR, i.e. shiftable appliances such as dishwashers and laundry machines, and non‐shiftable appliances such as lights and stoves. Although the shiftable appliances can be well controlled by energy management systems, the random usage patterns of non‐shiftable appliances will result in uncertainties to electrical demands and thus, affect the efficiency and reliability of smart grid operation. A two‐stage stochastic programming problem is formulated, for which the distribution system operation cost is minimised in the first stage, by considering various distribution system operation constraints. The scheduling of shiftable appliances is optimised in the second stage, by considering the random usage patterns of non‐shiftable appliances. To reduce the computational complexity caused by a large number of home appliances in distribution systems, scenario reduction technique is applied to reduce the number of possible scenarios while still retaining the essential features of the original scenario set. Extensive simulations are performed to evaluate the proposed DR scheme in IEEE 33‐bus and 119‐bus test distribution systems based on real appliance usage pattern data. [ABSTRACT FROM AUTHOR]

Since the overall energy consumption of residential buildings has significantly increased over the recent decades many technologies have been established to reduce the power consumption of households significantly and to use energy efficiently. In general, thermal energy for heating or cooling devices of domestic appliances is generated decentralized by the respective device and not in one central location of the building. This paper introduces a novel system design, where domestic appliances like the washing machine, dishwasher, condensing dryer, refrigerator and freezer are thermally interconnected to the heating and ventilation system in a low-energy building via the “energiBUS” – not only in terms of warm water, but also for cooling tasks. The integrated system concept is presented and load profiles are analyzed to discuss the functionality of the system and the energetic flows. The system benefits from the replacement of power consuming heating and cooling devices of the domestic appliances and the synchronized operation of the heat pump due to an additional hot and a cold water storage. In comparison to a system without the “energiBUS”, the overall power consumption of a four-person family home in passive house design is reduced by about 200 kWh/a without the improved heat pump synchronization. [ABSTRACT FROM AUTHOR]

Acinetobacter is a genus of Gram-negative bacteria, which are oxidase-negative, exhibiting a twitching motility under a magnifying lens. Besides being important soil microorganisms, due to their contribution to the soil fertility, Acinetobacter species, particularly A. baumannii, hold a prominent place within the genus because, it is the most virulent among the other species, causing varying degrees of human infections in clinical environments. However, results of different research have shown that Acinetobacter species can be isolated from such natural environments as surface water, wastewater and sewage, healthy human skin, plant, animal and food material as well as domestic appliances. The presence of some other Acinetobacter species in the natural environment has been associated with beneficial roles including soil improvement, detoxification of oil spillages and as microflora in human and plant bodies. In this paper, we carried out an overview of various natural ecological niches as reservoirs of pathogenic and non-pathogenic Acinetobacter species. [ABSTRACT FROM AUTHOR]

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Advances in metering and two‐way communication technologies foster the studies of Home Energy Management System (HEMS). This study proposes a new HEMS, which optimally schedules the distributed residential energy resources (DRERs) in a smart home environment with varying electricity tariff and high solar penetrations. The uncertainties of solar power output are captured by using Monte Carlo sampling technique to generate multiple solar output scenarios based on the probabilistic solar radiation model. The homeowner's rigid and elastic restrictions on the operations of the automatically controlled household appliances are modelled. Based on this, an optimal DRER scheduling model is proposed to minimise the home operation cost while taking into account the homeowner's requirements. A new heuristic optimisation algorithm recently proposed by the authors, i.e. natural aggregation algorithm, is used to solve the proposed model. Simulations based on real Australian solar data are conducted to validate the proposed method. [ABSTRACT FROM AUTHOR]