Household Load Research Articles

Electrical Control and Monitoring Home System With ACS712 Sensor & Relay Module via Blynk

Electrical load control and monitoring system is a device designed to control and monitor electricity consumption. The process of monitoring electric loads uses a power sensor model ACS712 which is connected to a relay module to supply electric loads and stop using electric loads if power consumption exceeds a predetermined capacity of 1000 watts so that energy control can be carried out. The microcontroller used is the NodeMCU esp8266 microcontroller which functions as the main control system that will be linked to the blynk application with the aim of being able to monitor it in real time in the future. By conducting an experiment assembling the ACS712 sensor module with NodeMCU esp8266 which produces a monitoring feature for reading current, power, energy and peak power measurements from the IoT blynk application through the ACS712 sensor. The results of this study are a control and monitoring of household electricity measurements through the blynk application to control the use of electrical energy loads from household electronic devices in the form of readings of current, power, energy, and stopping the supply of electric power to loads connected to the ACS712 sensor and relay. module. The Blynk IoT application, the ACS712 sensor is almost close to the measuring instrument or clamp meter used so that it is able to properly and correctly monitor electricity usage in household loads

Superior Optimization for a Hybrid PEMFC Power System Employing Model Predictions

This research investigated the impacts of model prediction on the optimization of hybrid energy systems using a system consisting of solar panels, batteries, a proton exchange membrane fuel cell (PEMFC), and a chemical hydrogen generation system. A PEMFC has several advantages, such as low operating temperatures, fast response times, high power density, and environmental friendliness, and it can convert hydrogen into electricity. However, because hydrogen costs are an important consideration, the PEMFC is usually integrated with hybrid energy systems to guarantee system sustainability. Therefore, in this study, a whole-year household load and solar radiation data were applied to optimize the system components and power management, thereby reducing the system cost by 42.43% and improving system sustainability by 7.05%. The system responses showed that some hydrogen consumption might be saved if the solar and load profiles could be foreseen. Two prediction models were developed that could accurately forecast the radiation and load profiles. Next, a second-year dataset was employed to verify the effectiveness of the model prediction. The results showed that the system cost was reduced by 40.20% without model prediction and by 44.06% with model prediction compared to the original system settings. Finally, experiments to illustrate the feasibility of the hybrid energy system were conducted using prediction models. Based on the results, the model prediction was deemed effective in improving the performance of hybrid energy systems.

Privacy-aware optimal load scheduling for energy management system of smart home

The wide deployment of smart meters has brought the significant challenge to privacy protection of residential users. However, the existing rechargeable battery schemes cannot protect the privacy when the consumer power usage is either high or low for a long period. A new energy management scheme for smart home is proposed to enhance the privacy protection ability while ensuring the electricity cost and comfort in the article. This article enhances privacy while addressing the joint optimization problem for smart home. Firstly, an energy management architecture incorporated the rechargeable batteries and renewable resources is constructed for enhancing privacy, where the user’s privacy is enhanced by utilizing the rechargeable batteries as well as the home appliance shifting. Secondly, the incentive probability is defined according to user satisfaction and the participation degree of demand response, and a probabilistic incentive mechanism is introduced to encourage more users to participate in household load schedule. Finally, the power cost, privacy as well as user satisfaction are systematically characterized in the objective function of the load scheduling, which is further decomposed by load classification. Then a hierarchical optimization method is leveraged to solve the optimal problem, maximizing the utilization of both residential users and power provider. The simulation results based on Pecan Street dataset validate the performance of the proposed strategy, and compared with the rechargeable battery schemes, the proposed scheme can save 19.72% of the electricity cost while ensuring privacy.

Application of Triple Active Bridge Converter for Future Grid and Integrated Energy System

Abstract: Renewable energy systems and electric vehicles (EVs) are receiving much attention in industrial and scholarly communities owing to their roles in reducing pollutant emissions. Integrated energy systems (IES), which connect different types of renewable energies and storages, have become common in many applications, such as the grid-connected photovoltaic (PV) and battery systems, fuel cells and battery/super capacitor in EVs. The advantages of all energy sources are maximized by utilizing connection and control strategies. Because many storage systems and household loads are mainly direct current (DC) types, the DC grid has considerable potential for increasing the efficiency of distribution grids in the future. In IES and future DC grid systems, the triple active bridge (TAB) converter is an isolated bidirectional DC-DC converter that has many advantages as a core circuit. In this paper reviews the characteristics of the TAB converter in current applications and suggests nextgeneration applications. First, the characteristics and operation modes of the TAB converter are introduced. An overview of all current applications of the TAB converter is then presented. The advantages and challenges of the TAB converter in each application are discussed. Thereafter, the potential future applications of the TAB converter with an adaptable power transmission design are presented.

Impact of Household PV Generation on the Voltage Quality in 0.4 kV Electric Grid—Case Study

This article (case study) discusses the influence of household photovoltaic generation on the voltage quality in a three-phase 0.4 kV grid. The research analyzes remotely acquired data from two specially designed three-phase Y-connected power meters to understand the PV system’s influence on the grid. The values of the voltages, currents, THD, individual harmonics, power factor, and K-factor are obtained every 200 ms over several months, creating more than 20 GB of data. The loads are typical household electrical appliances and EV portable chargers. The main conclusion after analyzing the measurements is that three-phase PV generation in the presence of household loads can create or increase grid unbalance; important current values into the neutral wire and THD and K-factor increase in some cases. The results also uncovered that without current sensing at the household connection point to the three-phase grid to control the PV inverter, the balanced phase output power of the grid-tied inverter is harmful to the 0.4 kV grid balance.

Smart Plug 2.0: A Solid-State Smart Plug Device Preventing Fire and Shock Hazards

Electrical faults cause fire or shock hazards in homes and offices. Flexible power cords are particularly susceptible to metal or insulation degradation that may lead to a variety of electrical faults. This article introduces a new Smart Plug 2.0 device which offers all-in-one protection against short-circuit, overload, arc, and ground faults in addition to the wireless power control and monitor function of conventional smart plug devices. It offers microsecond-scale time resolution to detect and respond to a fault condition, and significantly reduces the electrothermal stress on household electrical wires and loads. A new arc fault detection method is developed using machine learning models based on load current <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">di/dt</i> events. Comparing to other solid-state circuit breaker designs which typically respond to overcurrent conditions only, Smart Plug 2.0 integrates additional protective functions into the same hardware structure with minimal cost penalty. A 120 V/10 A solid-state Smart Plug 2.0 prototype using power <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">moseft</small> s is designed and tested. It has experimentally demonstrated the comprehensive protection features against all types of electrical faults.

Stochastic Nonlinear Model Predictive Control for a Switched Photovoltaic Battery System

Battery systems gain popularity among users in residential household setups. In this setup, currently the main source of profitability is to increase photovoltaic (PV) self-sufficiency which is highly dependent on the battery system efficiency. We present a control approach based on stochastic dynamic programming (SDP) suitable to increase the system efficiency. The optimization framework includes a switched system with standby losses, a nonlinear modeling of the converter losses as well as a stochastic forecast model for household load and PV generation. We show in a simulation of a typical benchmark case that our approach can in fact reduce overall system losses and costs of operation. Then, the applicability in a real-world scenario is shown using a commercially available battery system in a field test.

Improving the Efficiency of a Standard Distribution System Using the Correcting Power Factor Method

Most of the loads in the distribution system are inductive loads, due to the nature of household loads, most of which are reactive power consuming motors. These loads have a low power factor and cause voltage drop and increased power losses in the wires. The system most affected by power loss and voltage drop is the low-voltage distribution system of 0.4 kV, due to the large current that passes in this system. In this research, a simulation of a standard low-voltage radial distribution system (IEEE 30 Bus System) was carried out in the Power World Simulator Program in order to evaluate and improve the efficiency of the distribution system by manually installing capacitors at each consumer to correct the power factor to reduce voltage drop and electrical power losses in wires. The system simulation was conducted at constant loads, and the ineffective power was compensated for by its equivalent manually, by adding capacitors to keep the power factor close to Unity. The research aims to study the effect of power factor correction for each consumer on the overall distribution system efficiency, to be a basis for designing an automatic power factor correction unit that can be installed in homes, commercial buildings, and small factories. The simulation results were promising in terms of improving the overall system efficiency and reliability.

A Hybrid Heuristic Algorithm for Energy Management in Electricity Market with Demand Response and Distributed Generators

Optimal energy management trends are indispensable in improving the power grid’s reliability. However, power usage scheduling for energy management (EM) poses several challenges on a practical and technical level. This paper develops an energy consumption scheduler (ECS) to solve the power usage scheduling problem for optimal EM and overcome the major challenge in demand response (DR) implementation. This work aims to solve the power usage scheduling problem for EM to optimize utility bill, peak energy demand, and pollution emission while considering the varying pricing signal, distributed generators (DGs), household load, energy storage batteries, users, and EUC constraints. The ECS is based on a stochastic algorithm (genetic wind-driven optimization (GWDO) algorithm) because generation, DGs, demand, and energy price are stochastic and uncertain. The ECS based on the GWDO algorithm determines the optimal operation schedule of household appliances and batteries charge/discharge for a day time horizon. The developed model is analyzed by conducting simulations for two cases: home is not equipped with DGs, and home is equipped DGs in terms of utility bill, peak energy demand, and pollution emission. The simulation results validated the proposed model’s applicability to EM problems.

Collective self-consumption of solar photovoltaic and batteries for a micro-grid energy system

The paper carried out a techno-economic analysis in order to obtain the optimum battery storage capacity in conjunction with a photovoltaic array that can match the desired household load at highest possible self-consumption. In addition, it determined the economic feasibility of a proposed micro-system. Annual energy consumption, irradiance, and ambient temperature were measured at 1-min resolution for the year 2021. Simulations of a stationary economic model are performed for the years 2021 through 2030.The results showed that the photovoltaic array at 2.7 kWp capacity can generate an annual energy of approx. 4295.4 kWh and the optimal battery capacity which can satisfy 91.1% of self-consumption and energy cost of $0.256/kWh is 14.4 kWh. Furthermore, two third-order polynomial relationships between self-consumption and net present cost with energy cost were solved.

Classification of energy use patterns and multi-objective optimal scheduling of flexible loads in rural households

The significant stochastic volatility of renewable energy makes the regulation of flexible loads important in improving the matching degree of energy supply and demand. Under the background of urbanization, rural China is developing rapidly with an increasingly obvious trend of rural differentiation, resulting in different energy use patterns in different households. To accurately assess the flexibility potential, this study identified the use patterns of typical flexible load equipment based on the data from a field survey in the rural Guanzhong Plain, and classified rural households there accordingly. Giving full consideration to flexible loads, this study built a multi-objective optimal load scheduling model aimed at economic efficiency and user comfort. The non-dominated sorting genetic algorithm (NSGA-II) was applied to solve the proposed multi-objective optimization problem in MATLAB software. Then technique for order preference by similarity to an ideal solution (TOPSIS) method was to make the final decision from the Pareto frontier. According to the results of this study: 1) The usage patterns of washing machines were divided into morning usage pattern (Wm) and the late afternoon usage pattern (Wa). The charging patterns of scooters were classified into evening charging pattern (Ve) and night charging pattern (Vn). The usage patterns of air conditioners included midday usage pattern (ACm) and evening usage pattern (ACe). 2) The Wm-Vn-ACe households show greatest flexibility potential under the Scenario TOU (Time-of-use), the peak-valley difference of load curve is reduced by 22.9% compared with that before the optimal dispatching; The Wa-Vn-ACm households show greatest flexibility potential under the Scenario PVS (Photovoltaic system), the self-consumption increased by 10.7% compared with that before the optimal dispatching.3) Air conditioner usage patterns had a significant impact on the flexibility potential.

Efficient deep generative model for short-term household load forecasting using non-intrusive load monitoring

Home energy management systems (HEMS) enable key strategies and methods to improve residential efficiency and energy utilization. To make informed decisions, HEMS depend on energy monitoring and forecasting systems. However, short-term load forecasting (STLF) at the household level is challenging due to the high uncertainty in load demand caused by the difficulty of expecting customer behavior. Traditional solutions only use household load data at low temporal resolution and cannot adequately consider the users’ power consumption profile, limiting prediction accuracy. This paper addresses these issues and proposes a two-stage approach for the household STLF that leverages past and future appliance load data. In the first stage, the past and future appliance load data is estimated from the aggregate load measurement through a non-intrusive load monitoring (NILM) approach based on a deep generative model using variational autoencoders (VAE). This granular information is then augmented through a novel household load predictive model. We implement six configurations combining three stage-1 NILM models with two stage-2 predictive models based on VAE and temporal convolutional networks. Given the volatile nature of residential loads, identifying the running appliances and their consumption benefits the household STLF. The proposed approach is designed to leverage short-term appliance load forecasts rather than being limited to historical features, which is a key advantage, especially when multi-state appliances are used. The proposed approach is compared to state-of-the-art approaches on the UK-DALE and REFIT datasets and yields competitive results with improvements of 16% and 19% on average for the MAE and MAPE metrics, respectively.

Household debt load in Russia: A study of the problem and related factors

Subject. This article discusses the unstable economic situation in Russia and the policy of credit institutions. Objectives. The article aims to identify the causes and factors of rise in outstanding loans, including overdue to credit institutions. Methods. For the study, we used logical and statistical analyses. Results. The article finds that the loan debts of individuals, including overdue, is growing at a faster pace than the real disposable incomes of the population. Most regions of Russia are characterized by a high level of household debt load – over 50 percent. Conclusions. The level of debt load of the Russian population is dangerous both for financial institutions and for the national economy as a whole. The economic crisis, which has been going on since 2014, is the main reason for the household debt burden growth in Russia.

Optimal sizing of grid‐connected rooftop photovoltaic and battery energy storage for houses with electric vehicle

AbstractA practical optimal sizing model is developed for grid‐connected rooftop solar photovoltaic (PV) and battery energy storage (BES) of homes with electric vehicle (EV) to minimise the net present cost of electricity. Two system configurations, (1) PV‐EV and (2) PV‐BES‐EV, are investigated for optimal sizing of PV and BES by creating new rule‐based home energy management systems. The uncertainties of EV availability (arrival and departure times) and its initial state of charge, when arrives home, are incorporated using stochastic functions. The effect of popular EV models in the market is investigated on the optimal sizing and electricity cost of the customers. Several sensitivity analyses are adopted based on variations in the grid constrains, retail price and feed in tariff. Uncertainty analysis is provided based on the variations of insolation, temperature, and load to approve the optimal results of the developed model. A practical guideline is presented for residential customers in a typical grid‐connected household to select the optimal capacity of PV or PV‐BES system considering the model of EV. While the proposed optimization model is general and can be used for various case studies, real annual data of solar insolation, temperature, household's load, electricity prices, as well as PV and BES market data are used for an Australian case study. The developed optimal sizing model is also applied to residential households in different Australian States.

Multi-Objective Energy Management of a Smart Home in Real Time Environment

In home energy management, the occupants schedule the operating appliances to achieve lowest optimal energy cost with minimum discomfort. Smart home energy management turns out to be challenging due to the penetration of rooftop intermittent solar power generations. This article develops a multi-objective and real time smart residential load management and hence energy management topology considering variation in rooftop solar power generation with variation in price of distribution grid energy. Primarily, the optimization problem is established as a time average stochastic optimization formulation and then simplified to mixed integer linear programming (MILP) using Lyapunov optimization. The energy cost minimization objective is added with ocular and thermal discomfort minimization which also lowers the discomfort level for occupants due to actions caused by real time load management. The home loads are managed by an Internet-of-Things (IoT) based controller which also alerts the occupants for any incipient malfunction in the household loads. The proposed control technique is authenticated on a real-life smart residential data with simulations and experiments. The resultant controller can thus optimally control the smart residential loads in real time.

Secure Multi-Party Household Load Scheduling Framework for Real-Time Demand-Side Management

Renewable power sources are being increasingly incorporated into distribution networks. Therefore, demand-side management (DSM) has become more critical for improving system reliability. Currently, decentralized real-time DSM is practicable based on home energy management system (HEMS). However, coordinating these HEMSs is difficult because DSM customers may not wish to communicate with each other due to competition and privacy contents. A new peak may even emerge in the aggregator if HEMSs shift their loads without proper coordination. Hence, a secure multi-party household load scheduling framework was proposed in this study to ensure encrypted data sharing between HEMSs based on homomorphic encryption (HE) technology. In order to solve decentralized real-time DSM by directly using additive HE data in this proposed framework, a reinforcement learning (RL) method, namely boosting tree-based deep Q-network, was developed to be trained on a distributed algorithm. The results of case studies revealed that the proposed data-sharing framework outperformed the conventional DSM in shaving peak loads of the aggregator, whereas the electricity cost of each customer did not increase. Moreover, the proposed RL method protected the privacy of users and obtained a similar result compared with no-privacy-preserving RL methods.

Life cycle greenhouse gas emissions of residential battery storage systems: A German case study

AbstractBattery storage systems (BSSs) are popular as a means to increase the self‐consumption rates of residential photovoltaics. However, their environmental impact is under discussion, given the greenhouse gas emissions caused by the production and the efficiency losses during operation. Against this background, we carry out a holistic environmental assessment of residential BSSs by combining a partial life cycle assessment for the production phase with a detailed simulation of 162 individual German households for the operational phase. As regards the production phase, we only find small differences between the carbon footprints of different cell chemistries. Moreover, we can show that the balance of plant components have a comparable impact on the global warming potential as the cell modules. In terms of the operational phase, our simulations show that BSSs can compensate at least parts of their efficiency losses by shifting electricity demand from high‐emission to low‐emission periods. Under certain conditions, the operational phase of the BSSs can even overcompensate the emissions from the production phase and lead to a positive environmental impact over the lifetime of the systems. As the most relevant drivers, we find the exact emissions at the production stage, the individual household load patterns, the system efficiency, and the applied operational strategy.

Aggregate demand response strategies for smart communities with battery-charging/switching electric vehicles

Demand response (DR) plays an important role in the peak shaving and load-shifting of a community, but the avalanche effect creates a huge obstacle in DR with uncoordinated usage of appliances and charging of electric vehicles (EVs). Recognizing the remarkably rising deployment of EVs, there is great potential to coordinate EVs' charging in a charging station or a battery switching station for providing DR. However, the potential benefits of the battery-switching EVs for providing DR and their impacts on the aggregate load of the community have not been fully studied. In this regard, the present study proposes two novel aggregate community-level DR strategies, i.e., a strategy employing battery-charging EVs and a strategy employing battery-switching EVs. The proposed strategies are compared with the conventional strategy that only incorporates community battery energy storage for enabling peak shaving. The cost optimization is conducted with optimization parameters including demand limits during different periods and the power and energy capacity of the battery energy storage system. A stochastic bottom-up demand model for households, a probabilistic model for predicting loads of EVs, and a redistribution time-of-use tariff are implemented in the optimization. A group with 20 households and 20 EVs in New York, USA is used in the case study. The results in the case study show that the total annual costs (electricity tariff cost and battery energy storage cost if any) are reduced by 63.3 %, 54.0 %, and 14.7 % under the battery-switching EV strategy, the battery-charging EV strategy, and the battery energy storage strategy compared to the base case (i.e., DR-free strategy), respectively. Additionally, the battery-switching EV strategy enables DR throughout the day with the maximum temporal flexibility. Last, the developed two EV strategies can provide a good stability of the household loads on the grid and avoid the avalanche effect.

Maximum demand flexibility from the demand response of a big group of residential homes

Residential demand response (DR) programs are designed to reduce peak load from residential homes. It is essential for utilities to identify how much peak load could be potentially reduced from DR to optimally plan and operate the power system. However, it is extremely challenging to identify such a maximum load reduction, i.e., demand flexibility, offered by DR for a large group of residential homes due to the difficulties in modeling the willingness of customers to participate in DR programs and diverse loads in households. The willingness is also related to the incentives; a higher incentive can obviously attract more customers to participate. Targeting these problems, this paper provides an electricity cost minimization model for residential homes. DR incentives and customer willingness to respond to incentives are modeled to identify the realistic maximum demand flexibility. A case study using actual data from 98 homes at the Pecan Street microgrid database shows 9.7% of the controllable peak load (43 kW) can be reduced when the DR incentive is half of the peak electricity tariff. This demand flexibility increases to 66 kW, accounting for 14.9% of the controllable peak loads, when the incentive increases to twice of the peak tariff.

Electricity access in Uganda’s slums: multi-stakeholder perspectives from Kampala

Uganda’s rapid urbanisation poses service delivery challenges, particularly to the poorest neighbourhoods which are home to over half of the urban population. Only 63% of urban households have access to grid-electricity, and access deficits are most felt in slums despite their proximity to the grid. This study investigated the electricity access challenge in Uganda’s slums and the socio-political issues that shape access, from the perspective of multiple stakeholders. Using mixed methods, eight semi-structured interviews were conducted with key stakeholders and surveys (N = 35) conducted in Nakulabye slum, Kampala. The supply-side barriers identified were low consumption loads in households which makes electricity provision financially unviable for the utility, insecure land tenure and inadequate housing which present practical and safety barriers. High costs and tariffs constrain access, as does poor quality and unreliable supply, and inadequate infrastructure. Regardless, households use electricity beyond simply lighting and phone charging, and a variety of businesses in the slum rely on electricity. The interviews revealed tensions in the priorities of different stakeholder groups, with the regulator striving to balance the interests of the utility, policymakers, and end-users. Wider issues emerged as the politicization of service delivery, limited data and capacity of city councils, and influences from external actors and events. Understanding local contexts is crucial, because prevailing policy regimes and socio-political environments either stifle or foster electricity access for low-income groups. The energy injustices and access deficits were acknowledged by all respondents, but there were differences in opinion regarding the root causes of the challenge, which could have negative implications for solution-finding efforts. Generally, the extent of the challenges faced is unknown outside the slum communities, and future work could explore the lived experiences of electricity access, to inform equitable electricity provision.