Search Results (86)

Residential photovoltaic and battery energy storage systems (PV/BESS systems) are gaining attention as a measure against natural disasters and rising electricity prices. This paper aims to propose an operational strategy that balances electricity cost reductions, battery lifespans, and outage mitigation for the residential PV/BESS system. The optimization model considering battery degradation determines normal operations with balancing cost reductions and degradation. Additionally, a rule-based approach simulates system performance during various outages and evaluates supply continuity using a resilience metric: the percent continuous supply hour. Outage mitigation benefits are quantified by considering the distribution of residential values of lost load (VoLLs). Results show that the operation considering degradation maintains a high state of charge (SoC) at all times. For 25.7% of households with large demand, electricity cost reductions exceed equipment costs. Outage simulations demonstrate that the mean energy supplied during a 48-h outage ranges from 14 kWh to $26.7$ kWh. Furthermore, the proposed operation increases the resilience metric from 20% to 30% under severe and unpredictable outages. Finally, incorporating outage mitigation benefits increases the proportion of households adopting PV/BESS systems by 21.5% points. Full article

This study investigates the accelerating adoption of electric vehicles (EVs) integrated with residential rooftop solar and battery storage in Australia, employing Actor–Network Theory (ANT) to elucidate socio-technical dynamics. Through purposive sampling, semi-structured interviews with 15 EV industry stakeholders were conducted and analysed using NVivo 14. Findings revealed EV–solar–storage adoption as a negotiated process shaped by alignments among human and non-human actors, structured by three interdependent obligatory passage points. First, technological integration hinges on interoperability among inverters, smart chargers, EV supply equipment, batteries, and home energy management systems. These are constrained by factors like off-street parking availability. Second, policy and market frameworks require clear interconnection standards, bidirectional charging protocols, streamlined approvals, and targeted incentives. Third, consumer engagement depends on energy literacy, equitable access for renters, and daytime charging infrastructure. Smart and bidirectional charging positions EVs as flexible energy assets, yet gaps in standards and awareness destabilise networks. This ANT-framed study offers a practice-oriented model for clean mobility integration, proposing targeted interventions such as device compatibility standards, equitable policies, and education to maximise environmental and economic benefits at household and system levels. Full article

Modern electrical vehicles (EVs) are equipped with sizable batteries that possess significant potential as energy prosumers. EVs are poised to be transformative assets and pivotal contributors to the virtual power plant (VPP), enhancing the performance and profitability of VPPs. The number of household EVs is increasing yearly, and this poses new challenges to the optimization of VPP operations. The computational cost increases exponentially as the number of decision variables rises with the increasing participation of EVs. This paper explores the role of a large number of EVs as prosumers, interacting with a VPP consisting of a photovoltaic system and battery energy storage system. To accommodate the large quantity of EVs in the modeling, this research adopts the decentralized control structure. It optimizes EV operations by regulating their charging and discharging behavior in response to pricing signals from the VPP. A two-stage optimization framework is proposed for VPP-EV operation using a reinforcement algorithm and gradient-based programming. Action masking for reinforcement learning is explored to eliminate invalid actions, reducing ineffective exploration, thereby accelerating the convergence of the algorithm. The proposed approach is capable of handling a substantial number of EVs and addressing the stochastic characteristics of EV charging and discharging behaviors. Simulation results demonstrate that the VPP-EV operation optimization increases the revenue of the VPP and significantly reduces the electricity costs for EV owners. Through the optimization of EV operations, the charging cost of 1000 EVs participating in the V2G services is reduced by 26.38% compared to those that opt out of the scheme, and VPP revenue increases by 27.83% accordingly. Full article

Voltage supraharmonics present in the electrical grid can trigger chain reactions in grid-connected household and industrial power supplies equipped with Power Factor Correction (PFC). A single source of voltage supraharmonics may significantly increase the current in switching devices with PFC, leading to higher-amplitude disturbances throughout the electrical network. When addressing issues in a real low-voltage (LV) grid, it was observed that activation of a single device emitting supraharmonics caused oscillating currents across all feeders connected to the transformer’s busbars, matching the frequency of the supraharmonic source. To investigate this phenomenon further, the grid voltage containing supraharmonics was replicated in a controlled laboratory environment and used to supply various power electronic devices. The laboratory results closely mirrored those observed in the field. Supraharmonics present in the supply voltage caused current oscillations in the power electronic devices at the same frequency. Moreover, the amplitude of the observed current oscillations increased with the amplitude of the injected supply voltage supraharmonics. In some cases, the root mean square (RMS) value of the current drawn by the power electronic devices doubled, indicating a substantial impact on device behaviour and potential implications for grid stability and energy efficiency. Full article

Household electricity meters equipped with rooftop photovoltaic systems only display net load power data after coupling loads with photovoltaic power, which gives rise to the issue of unknown PV output and load demand. A non-invasive load decomposition algorithm based on Improved Density Peak Clustering (IDPC) and the Simplified Hidden Markov Model (SHMM) is proposed to decompose PV generation power and load consumption power from net load power data, providing data support for power demand-side management. First, the Improved Density Peak Clustering algorithm is used to adaptively obtain load power templates. Then, historical power data from PV proxy sites are classified based on weather types, while radiation proxies are used to estimate the historical PV power of the target users. These estimated PV power data are combined with historical load information to derive the parameters of the SHMM under different PV output conditions, thereby constructing the load decomposition objective function. Finally, the net load power data are used to achieve non-intrusive load decomposition and photovoltaic power extraction for households with PV systems; the effectiveness of the proposed algorithm is validated using Apmds datasets and Pecans Street datasets. Full article

The reliability of power supply systems is of utmost importance for telecommunications. In our daily lives, we are used to having constant access to the power grid with negligible risks. Standards and practices established over the years guarantee minimal problems for the household consumer and accidents in their electrical appliances. Often, the biggest inconvenience of a power failure for the average person is having to set the clock on the stove or use the flashlight on their phone. However, we rarely realize how fragile the balance on which all this is based is, but telecom companies are fully aware of this fact. Regardless of whether the problem comes from natural phenomena, accidental or intentional damage, or defects in the equipment, the equipment used in telecommunications technologies is extremely sensitive, and it is necessary to take protective measures. Full article

Due to the increasing volumes of plastic waste generated from electric and electronic devices, research has focused on the investigation of recycling methods for their safe handling. Pyrolysis converts plastics from waste electric and electronic equipment (WEEE) into valuable products (pyrolysis oil). Nevertheless, the frequent presence of flame retardants, mainly brominated flame retardants (BFR), hinders pyrolysis’s wide application, since hazardous compounds may be produced, limiting the use of pyrolysis oils. Taking the aforementioned into account, this work focuses on the recycling, via pyrolysis, of various plastic samples gathered from WEEE, to explore the valuable products that are formed. Specifically, 14 plastic samples were collected, including parts of computer peripheral equipment, remote controls, telephones and other household appliances. Considering the difficulties when BFRs are present, the study went one step further, applying XRF analysis to identify their possible presence, and then Soxhlet extraction as an environmentally friendly method for the debromination of the samples. Based on the XRF results, it was found that 23% of the samples contained bromine. After each Soxhlet extraction, bromine was reduced, achieving a complete removal in the case of a remote control sample and when butanol was the solvent. Thermal pyrolysis led to the formation of valuable products, including the monomer styrene and other secondary useful compounds, such as alpha-methylstyrene. The FTIR results, in combination with the pyrolysis products, enabled the identification of the polymers present in the samples. Most of them were ABS or HIPS, while only three samples were PC. Full article

With the development of society, energy application and building thermal comfort in rural residences are receiving more and more attention. The rural residences in this survey mainly cover the rural areas of 21 prefectures in Guangdong province, of which 24.7% are in the Pearl River Delta, 18.9% in western Guangdong, 13.1% in eastern Guangdong, and 43.2% in northern Guangdong. Rural household energy consumption is mainly used for lighting equipment, household appliances, and cooking equipment, where lighting equipment and household appliances mainly consume electrical energy, and cooking equipment consumes different types of energy due to the diversity of types. First, there is a wide variety and variation in rural energy consumption, with electricity and liquefied petroleum gas as the main sources of cooking energy. Hot water is mainly obtained by heating with electricity and natural gas. Secondly, for rural residents, renewable energy is too expensive to build, is also affected by the environment and weather, and is often not convenient to use. Third, rural residents generally experience a warm, humid indoor environment with adequate airflow, but poor kitchen ventilation reduces air quality satisfaction. To enhance renewable energy adoption, technological advancements and cost reductions are necessary, along with increased government efforts in awareness campaigns, policy incentives, and demonstration projects. This study analyses the rural energy structure in Guangdong, proposes the direction of rural energy optimization, and analyses rural energy use and the feasibility of renewable energy promotion, considering the population and income of rural households. Full article

Standby power consumption in household electrical and electronic equipment remains a persistent source of energy waste worldwide. Despite regulatory measures and ongoing technological developments, a considerable amount of electricity is still consumed by devices in standby or “off-mode”, resulting in higher utility costs and carbon emissions. This review synthesizes the latest research to clarify the scale of standby energy consumption, discusses relevant policies and regulations, and explores intelligent technologies and behavioral strategies that minimize energy consumption. Starting from the theoretical analysis and modeling of equipment consumption in standby mode to the implementation of intelligent systems to reduce it, the paper highlights heuristic optimization methods, smart grid integration, and occupant-centered interventions, all of which demonstrate tangible energy savings. This research was carried out in close connection with current policies regarding energy consumption and sustainable development, respectively, with the implementation of new technologies. Thus, in accordance with the latest European directives, the intelligent systems used have reduced the energy consumption of some common household appliances by 26.68 kWh. Additionally, knowledge gaps, particularly regarding user behavior, data granularity, and the integration of advanced analytics that limit the efficacy of current solutions, are identified. Recommendations for future research, emphasizing the importance of harmonized policies, precise data measurement, and artificial-intelligence-driven approaches for further reducing standby loads, are finally presented. Full article

This research introduces an original approach to time series forecasting through the use of multi-scale convolutional neural networks with Transformer modules. The objective is to focus on the limitations of short-term load forecasting in terms of complex spatio-temporal dependencies. The model begins with the convolutional layers, which perform feature extraction from the time series data to look for features with different temporal resolutions. The last step involves making use of the self-attention component of the Transformer block, which tries to find the long-range dependencies within the series. Also, a spatial attention layer is included to handle the interactions among the different samples. Equipped with these features, the model is able to make predictions. Experimental results show that this model performs better compared to the time series forecasting models in the literature. It is worth mentioning that the MSE score or mean square error of the model was 0.62, while the measure of fit R² was 0.91 in predicting the individual household electric power consumption dataset. The baseline models for this dataset such as the LSTM model had an MSE of 2.324 and R² value of 0.79, showing that the proposed model was significantly improved by a margin. Full article

This research focuses on analyzing the aerodynamic characteristics of residual air currents generated by vehicle movement and evaluating their feasibility for energy generation, then designing a vertical axis wind turbine. The parameters assessed include the characteristic velocity profile, the average and maximum velocities, disturbance lifetimes, as well as the frequency and probability of recurrence of these disturbances. Using the data, projections are made on the electrical energy amount that can be produced by a wind turbine operating under such wind conditions. Measurements were taken at four locations: three within Mexico City (CDMX) and one on the outskirts. The measurement station, consisting of a 2.35 m vertical tower equipped with eight vertically aligned thermos-resistive anemometers, is installed on medians less than 0.50 m from moving vehicles. The data from within CDMX show maximum wind velocities ranging from 6 to 8 m/s at ground level, while measurements on the outskirts record velocities of up to 19.5 m/s. A probabilistic analysis reveals that usable air currents could be present 58% of the time. Based on electrical production calculations, it is estimated that harnessing this residual energy could power approximately 4500 homes, considering the national cost per kWh and the average electricity consumption of a four-person household. Full article

The rapidly growing global energy demand, environmental concerns, and the urgent need to reduce carbon footprints have made sustainable household energy consumption a critical priority. This study aims to analyze household energy data to predict the electricity self-sufficiency rate of households and extract meaningful insights that can enhance it. For this purpose, we use LightGBM (Light Gradient Boosting Machine)-, SHAP (SHapley Additive exPlanations)-, and correlation-heatmap-based approaches to analyze 12 months of energy and questionnaire survey data collected from over 200 smart houses in Kitakyushu, Japan. First, we use LightGBM to predict the ESSR of households and identify the key features that impact the prediction model. By using LightGBM, we demonstrated that the key features are the housing type, average monthly electricity bill, presence of floor heating system, average monthly gas bill, electricity tariff plan, electrical capacity, number of TVs, cooking equipment used, number of washing and drying machines, and the frequency of viewing home energy management systems (HEMSs). Furthermore, we adopted the LightGBM classifier with ${\ell }^1$ regularization to extract the most significant features and established a statistical correlation between these features and the electricity self-sufficiency rate. This LightGBM-based model can also predict the electricity self-sufficiency rate of households that did not participate in the questionnaire survey. The LightGBM-based model offers a global view of feature importance but lacks detailed explanations for individual predictions. For this purpose, we used SHAP analysis to identify the impact-wise order of key features that influence the electricity self-sufficiency rate (ESSR) and evaluated the contribution of each feature to the model’s predictions. A heatmap is also used to analyze the correlation among household variables and the ESSR. To evaluate the performance of the classification model, we used a confusion matrix showing a good F1 score (Weighted Avg) of 0.90. The findings discussed in this article offer valuable insights for energy policymakers to achieve the objective of developing energy-self-sufficient houses. Full article

Dynamic energy contracts, offering hourly varying day-ahead prices for electricity, create opportunities for a residential Battery Energy Storage System (BESS) to not just optimize the self-consumption of solar energy but also capitalize on price differences. This work examines the financial potential and impact on the self-consumption of a residential BESS that is controlled based on these dynamic energy prices for PV-equipped households in the Netherlands, where this novel type of contract is available. Currently, due to the Dutch Net Metering arrangement (NM) for PV panels, there is no financial incentive to increase self-consumption, but policy shifts are debated, affecting the potential profitability of a BESS. In the current situation, the recently proposed NM phase-out and the general case without NM are studied using linear programming to derive optimal control strategies for these scenarios. These are used to assess BESS profitability in the latter cases combined with 15 min smart meter data of 225 Dutch households to study variations in profitability between households. It follows that these variations are linked to annual electricity demand and feed-in pre-BESS-installation. A residential BESS that is controlled based on day-ahead prices is currently not generally profitable under any of these circumstances: Under NM, the maximum possible annual yield for a 5 kWh/3.68 kW BESS with day-ahead prices as in 2023 is EUR 190, while in the absence of NM, the annual yield per household ranges from EUR 93 to EUR 300. The proposed NM phase-out limits the BESS’s profitability compared to the removal of NM. Full article

Residential consumption represents one of the most important percentages of total electricity consumption. A considerable number of household appliances consume energy even when they are not in operation, i.e., they are in the so-called standby state, thus producing additional costs, which become significant over time. In this context, one method to solve this problem is to develop a smart system capable of severing the power connection to devices in standby mode, thereby conserving energy and reducing the energy costs. The first step in the design of this system consists of the identification and accurate measurement of the standby state, which was carried out for three of the most common household appliances. Then, by using an ESP32 microcontroller, a system was designed to manage the operation of a relay module based on the current consumption of the connected equipment. Control over the system was achieved through a web application that works across all devices equipped with a web browser, offering functionalities to adjust current value time delays and to manually switch the system on or off. Finally, the deployment of this system across the three appliances studied led to a reduction in the energy consumption in standby mode of 26.68 kWh per month. Full article

Ireland experiences high energy poverty rates alongside surplus wind energy resources. With 77% of Irish households equipped with electrical immersion heaters for domestic hot water (DHW) generation, this study proposes an Electrical Water Heating Aggregation (EWHA) scheme. The scheme allocates surplus wind-generated electricity to provide DHW to fuel-poor households, thereby alleviating energy poverty through harnessing curtailed wind energy. Through a developed wind-generated electricity allocation model and half-hourly data analysis for a weather year, this research assesses the feasibility and economic viability of the EWHA scheme, focusing on the householder as the primary benefactor from the scheme (as opposed to ancillary grid service provision). The results suggest an optimal aggregation size where maximum curtailment and carbon offset coincide with maximum benefits for participants. The findings indicate that fuel-poor households in Ireland could receive a full DHW tank every three weeks using surplus wind energy, harnessing 89% of overnight curtailed wind energy and offsetting 33 MkgCO₂ annually. Moreover, the scheme could potentially save the Irish state approximately EUR 4 million by 2030, increasing to EUR 11 million by 2050, in carbon costs. Overall, this research demonstrates the potential of EWHA schemes to alleviate energy poverty, optimise wind energy utilisation, and contribute significantly to carbon emission reduction targets. Full article