Electricity Usage Research Articles

Design and operational strategy optimization of a hybrid electric heating system with phase change materials for energy storage in nearly zero energy buildings

Nearly zero energy buildings (nZEBs) and the associated research on heating energy systems are gaining increasing attention. To enhance PV self-consumption capacity in nZEBs, a hybrid electric heating system with phase change materials (PCM) for energy storage using photovoltaic (PV) and grid power was developed. To study the system's performance, an experimental bench was set up, and mathematical models for energy efficiency and operational strategy were developed. Furthermore, a research building was chosen as the scenario to investigate the operational performance of the system. The analysis focused on assessing the impact of varying PV and battery capacity, specifically concentrating on load flexibility transfer capability and energy flow within the system. The results indicated that when implementing the hybrid electric heating system with varying PV and battery capacities, significant transfers of both heat and electricity loads could be achieved, leading to peak price electricity usage remaining below 10 %. Regardless of the manner in which PV is utilized, the system facilitates the transfer of both heat and electricity loads, resulting in peak price electricity usage remaining below 5 %. Through an analysis involving the optimization of system capacity and operational strategies during a simulated one-week period in the heating season, the results revealed that the utilization of a battery with a rated capacity as low as 3.69 kWh led to an increase of 10.93 kWh in PV self-consumption, resulting in a noteworthy cost reduction of 15.6 %.

Distributed energy management of electric vehicle charging stations based on hierarchical pricing mechanism and aggregate feasible regions

With the rapid development of electric vehicle charging stations, effective management of them has become challenging due to the high uncertainty of electric vehicles, the pricing mechanisms of charging stations, and their coupling with distribution networks. To address these challenges, this paper proposes a two-stage framework for energy management at charging stations. In the first stage, a resource allocation model considering the profits of distribution systems, charging stations, and electric vehicle users is established based on the aggregate feasible power regions of charging stations. The aggregate feasible region is obtained based on the combination of Minkowski summation and the data-driven method, which can preserve the privacy of electric vehicle data and reduce the computational burden. In the second stage, a novel hierarchical pricing mechanism is developed, which encompasses both the clearing price between charging stations and distribution networks and the retail electricity price between charging stations and electric vehicle users. Notably, charging stations participate in the power clearing of distributed networks based on the aggregate feasible power region, while a two-stage robust pricing strategy is established between electric vehicle users and charging stations. The model is finally optimized through a distributed coordination mechanism with a clear physical interpretation. The simulation results show that the proposed aggregation method enables charging stations to achieve a total economic profit at least 1.76 % higher than three competitive methods. The hierarchical pricing mechanism allows charging stations to achieve total economic profits 18.60 % and 2.94 % higher than those in the centralized dispatch and price-taker modes, respectively, while simultaneously reducing operating costs for the distributed network by 25.96 % and 27.99 %.

Household electricity consumption determinants in major Nigeria cities

The increasing epileptic electricity supply, mainly in the residential areas of Nigerian cities, has been linked to the incorrect knowledge of the numerous socio-economic and physical indices that influence household electricity usage. Most of the seemingly identified explanatory factors were done at macro level which does not give a clear estimate of this electricity demand. The thrust of the study is to analyse empirically the household electricity determinants in Nigerian cities with a view to evolving a more informed and sustainable energy policy decision. Multistage area cluster sampling method was adopted in the study where 769 copies of structured questionnaire were distributed to electricity users of prepaid meters in five major Nigerian cities. The research hypothesis was tested using the multiple linear regression statistical tool. The result revealed that nine variables which include age (r = 0.05, p-value: 0.05), household income (r = 0.00, p-value: 0.05), number of hours that people stay outside the house (r = 0.043, p-value: 0.05), number of teenagers at home, (r = 0.006, p-value: 0.01) number of electrical appliances (r = 0.016, p-value: 0.01), type of house (r = 0.012, p-value: 0.01), hours that the electrical appliances are used (r = 0.043, p-value: 0.05), weather condition, (r = 0.011, p-value: 0.05) and the location of the building (r = 0.045, p-value: 0.05) were significant in determining the household electricity consumption. Policies based on the findings will give energy and urban planners an empirical basis for accurate and robust forecasting of the determinants that influence household electricity consumption in Nigeria that is devoid of any speculation or unfounded predictions.

Investigating Individual’s Energy Saving Behavior in Using Electric Vehicles: Extended Theory of Planned Behavior

The rapid climate change and its negative impacts have garnered global attention towards carbon emission mitigation efforts. The use of electric vehicles and individual energy-saving behaviors plays a crucial role in reducing carbon emissions from the transportation sector. The aim of this research is to analyze the influence of individual attitudes towards behavior, subjective norms, and perceived behavioral control on the intention and behavior of energy-saving, particularly in the context of electric vehicle usage. This study employs a quantitative approach and a survey method using a Likert-scale questionnaire. The sample of respondents consists of electric vehicle users selected through random sampling. Data was collected through questionnaires with a scale ranging from 1 to 5, and a total of 313 samples were used for analysis. Data analysis was conducted using Structural Equation Model (SEM) with the assistance of SmartPLS 4 software. The results of the analysis indicate that an individual's attitude towards behavior plays a significant role in influencing the intention and behavior of energy-saving. Perceived behavioral control also directly influences energy-saving behavior. However, subjective norms do not have a significant impact on intention and energy-saving behavior. These findings suggest that to promote individual energy-saving behavior, there should be an emphasis on cultivating positive attitudes towards energy-saving practices and factors affecting behavioral control. This research provides valuable insights into the factors influencing individual energy-saving behavior in the context of electric vehicle usage. With a better understanding of these factors, we can contribute to global efforts to reduce carbon emissions and mitigate climate change.

Impact of the Saudi Building Code on Residential Construction Costs

The government of Saudi has implemented the Saudi Building Code (SBC) for residential buildings to set standards for their design, construction, and maintenance. However, this may result in unexpected costs that are passed on to the citizens and thus have a certain economic impact. Therefore, this study examined the additional costs imposed on citizens and the economic impact of implementing the SBC on residential buildings. This study surveyed building developers, architects, and citizens to gather data on the costs associated with adhering to the code such as material and labor costs. This study found that the additional costs imposed on citizens and the economic impact of implementing the SBC in residential buildings were relatively small. The potential long-term benefits of the SBC such as energy efficiency savings and reduced environmental impact outweigh the short-term costs. The case that applied the SBC had operating costs that were almost 44% lower. Energy efficiency was increased by the SBC’s contribution to reducing heat transfer through building components and external elements. In order to minimize heat absorption and electricity usage, thermal insulation, double and heat-reflecting glass, and light exterior colors are all recommended. The findings of this research provide valuable insights for policymakers and stakeholders in the construction industry because they consider the implementation and enforcement of building codes in the Kingdom of Saudi Arabia.

Learning-based scheduling of integrated charging-storage-discharging station for minimizing electric vehicle users' cost

Charging cost is an important concern for electric vehicle (EV) users. The ordered charging behavior, such as the reasonable selection of charging period and charging power, can greatly decrease users' charging cost. Towards the integrated charging-storage-discharging station (ICSDS), a learning-based method is proposed in this paper to minimize EV users' cost. The physical constraints of ICSDS and the user's demand are first built, and the charging scheduling problem of ICSDS is formulated as a Markov Decision Process (MDP) with unknown transition probability. Second, in order to generate optimal schedule through the learning network, the deep features of the future electricity price are extracted by using a long short-term memory (LSTM) network. Third, a twofold deep deterministic policy gradient (TDDPG) algorithm is proposed to generate the continuous charging actions and avoid the Q value overestimation. In addition, the TDDPG-based scheduling strategy is designed on the basis of the extracted features of electricity price. Finally, the validations through the real-world data demonstrate that the features of electricity price are extracted with a satisfied accuracy. Moreover, compared with many benchmarked methods, the experimental results demonstrate that the charging scheduling by TDDPG possesses excellent performance in minimizing EV users' cost.

The Relationship between Electricity Consumption, Price of Oils, and Gross Domestic Product in the Gulf Cooperation Council Countries

The research aimed to evaluate the impact of electricity consumption and oil prices on the GDP of GCC countries. The oil industry is the backbone of economies in these countries, accounting for a significant portion of GDP. The variations and volatility in petroleum prices significantly affect GCC countries’ GDP. Moreover, these countries have also increased the usage of electricity. A substantial increase in the demand for electricity impacts the economic development of GCC countries. The study has considered a quantitative method using secondary data to address the research objective. The data for electricity consumption, energy use, and oil prices were obtained from the World Development Indicators website from 2010 to 2021. The countries include Bahrain, Oman, Qatar, the United Arab Emirates (UAE), Saudi Arabia, and Kuwait. The data obtained were empirically analyzed using the system-GMM approach as it was identified to be a more robust estimation model controlling for issues such as autocorrelation and endogeneity. The results obtained from the system-GMM indicated that electricity consumption and oil prices positively impact the GDP of GCC countries. At the same time, energy use hurts the GDP. Therefore, the research findings imply that electricity consumption and oil prices significantly contribute to the growth of the GCC countries.

Sustainable Energy Consumption Insights: Understanding Electricity-saving Behaviour Drivers among Young Adults in Ho Chi Minh City

As Vietnam’s population continues to grow, there is a growing need for sustainable societal development. The energy-saving behaviour of every citizen plays a crucial role in achieving this sustainability. Therefore, it is necessary to raise awareness among young adults about energy conservation and environmental protection to promote sustainable social development in Vietnam. Hence, this study aims to examine the key factors influencing electricity-saving and habitual energy-saving behaviour. Data were collected from 431 young adults aged 18 to 30 years old in Ho Chi Minh City. The results indicate the significance of the model studied, with all hypotheses being accepted. The findings demonstrate that several factors, including subjective standards, perceived behavioural control, awareness of consequences, sense of responsibility, personal moral norms and cognition of electricity prices, influence electricity-saving behaviours and habitual energy-saving behaviour related to electricity usage. The findings suggest that young adults are increasingly recognising the importance of conserving electricity in modern life to protect the environment and promote sustainable social development in Vietnam. The management implication suggests that individuals in Ho Chi Minh City have demonstrated an awareness of energy conservation. However, responsible authorities should initiate programmes aimed at promoting electricity-saving practices. This includes the creation of media articles to raise awareness and enhance the understanding of electricity-saving measures, serving as reminders to increase awareness about conserving electricity among every individual. Furthermore, this study offers theoretical and practical implications for researchers and policymakers alike.

Switching gears: Evaluating Grid-to-Vehicle (G2V) and battery swap (BS) behaviors in new energy vehicles (NEVs) within a flexible institutional landscape

The transition to electric vehicles is a pivotal step towards achieving emission reductions and realizing the “double carbon” objectives in the transportation sector. This movement is heavily dependent on the development of a strong G2V and BS infrastructure, a task which embodies the motivation behind this study. As such, we seek to decipher the intrinsic challenges and opportunities resident in the G2V and BS systems, constructing vital questions around existing impediments and strategies that would foster a seamless transition to NEVs, integrating global insights and experiences from different nations into our enquiry. In view this, this paper proposes a tripartite institutional logic framework, which serves as a foundational structure encompassing government, automobile manufacturing enterprises, and consumers as pivotal stakeholders within an interconnected ecosystem. Building upon this comprehensive framework, we further advance our analysis by constructing a tripartite evolutionary game model and using a simulation with a case study to analyze the dynamic interplays of G2V and BS systems. The results show that: The BS mode has a higher efficacy in terms of energy efficiency and optimizing electricity usage compared to the G2V model; Proper subsidies for BS are necessary, as supported by simulations and case studies; The government must continue to support enterprise R&D efforts to advance the NEV industry; Increasing investment in infrastructure is essential to address issues related to the layout and compatibility of BS facilities among auto enterprises; Recognizing the positive impact of BS mode on the social and environmental aspects is vital; Accurate identification of consumer needs is crucial for promoting BS mode. Finally, policy recommendations are proposed to promote transportation sector, which provides theoretical support for policy makers.

Multi-layer and multi-source features stacking ensemble learning for user profile

Electricity bill-sensitive user profiling in the power industry is gradually being recognized as a research hotspot. A multi-layer feature construction method has been proposed, separating the mining of textual and numerical information, addressing the insufficient exploration of textual data in existing user profile processing methods. The complexity of electricity user profiles is addressed through the introduction of a two-stage predictive model based on Stacking ensemble learning. In the first stage, user sensitivity is predicted by utilizing the advantages of MLP (Multi-Layer Perceptron), CNN-LSTM (Convolutional Neural Network-Long Short-Term Memory), and XGB (Extreme Gradient Boosting) in global, local, and missing value handling, respectively. In the second stage, electricity-sensitive users are identified by employing RF (Random Forest). The experimental results show that the MLS-SEL user profile model is higher than the models MVEM, SG and SMUPM in terms of both F1 value and accuracy rate. It is implied that users who could be more sensitive to fluctuations in electricity costs have been identified more accurately.

Toward sustainable cities: Where the electric vehicle users reside matters

This paper investigates how different location patterns of electric vehicle (EV) users affect the outcomes of an urban-space economy. In a linear city where firms and residents compete for land, I show that the location patterns of EV users exert significant impact on traffic pollution externalities and urban land rent revenues. In particular, the urban spatial structure with EV users residing in the suburbs (instead of the center) is associated with a better urban environment and higher land rent revenues. From society’s point of view, the increased land rents may be levied by governments to subsidize suburban residents in buying EVs or to invest in building charging stations, achieving higher efficiency.

Residential load forecasting by a PSO-tuned ANFIS2 method considering the COVID-19 influence

The most important feature of load forecasting is enabling the building management system to control and manage its loads with available resources ahead of time. The electricity usage in residential buildings has increased during the COVID-19 period, as compared to normal times. Therefore, the performance of forecasting methods is impacted, and further tuning of parameters is required to cope with energy consumption changes due to COVID-19. This paper proposes a new adaptive neuro-fuzzy 2 inference system (ANFIS2) for energy usage forecasting in residential buildings for both normal and COVID-19 periods. The particle swarm optimization (PSO) method has been implemented for parameter optimization, and subtractive clustering is used for data training for the proposed ANFIS2 system. Two modifications in terms of input and parameters of ANFIS2 are made to cope with the change in the consumption pattern and reduce the prediction errors during the COVID-19 period. Simulation results obtained by MATLAB software validate the efficacy of the proposed ANFIS2 in residential load forecasting during both normal and COVID-19 periods. Moreover, the performance of the proposed method is better than that of the existing adaptive neuro-fuzzy inference system (ANFIS), long short-term memory (LSTM), and random forest (RF) approaches.

Enhancing Residential Electricity Safety and Management: A Novel Non-Intrusive Load Monitoring-Based Methodology for Accurate Appliance Operational State Identification

Non-intrusive load monitoring (NILM) technology, crucial for intelligent electricity management, has gained considerable attention in residential electricity usage studies. NILM enables monitoring of total electrical current and voltage in homes, offering insights vital for enhancing safety and preventing domestic electrical accidents. Despite its importance, accurately discerning the operational status of appliances using non-intrusive methods remains a challenging area within this field. This paper presents a novel methodology that integrates an advanced clustering algorithm with a Bayesian network for the identification of appliance operational states. The approach involves capturing the electrical current signals during appliance operation via NILM, followed by their decomposition into odd harmonics. An enhanced clustering algorithm is then employed to ascertain the central coordinates of the signal clusters. Building upon this, a three-layer Bayesian network inference model, incorporating leak nodes, is developed. Within this model, harmonic signals are used as conditions for node activation. The operational states of the appliances are subsequently determined through probabilistic reasoning. The proposed method’s effectiveness is validated through a series of simulation experiments conducted in a laboratory environment. The results of these experiments (low mode 89.1%, medium mode 94.4%, high mode 92.0%, and 98.4% for combination) provide strong evidence of the method’s accuracy in inferring the operational status of household electrical appliances based on NILM technology.

Renewable-based load shifting system for demand response to enhance energy-economic-environmental performance of industrial enterprises

A market characterized by economic uncertainty and increased competition has led industrial enterprises to find ways to improve operational efficiency and reduce energy costs. In this context, the Demand Response (DR) has drawn ever greater attention from industries, allowing the shift of the electricity usage from their normal consumption patterns in response to changes in the price of electricity over time. This study proposes an innovative control strategy for renewable-based Load Shifting (LS) system designed on, at the same time, energy, economic, and environmental performance to realize an effective DR for industrial enterprises. The proposed techno-economic model is based on power flow management, economic indicators, such as NPV, IRR, and DPP, and associated CO2 emissions, adopting both deterministic and stochastic approaches. A realistic government incentive program framed in a potential carbon-based market is also assessed and validated. The resulting solution is obtained through a sensitivity analysis of several parameters, including the operating conditions of 15%, 20%, 30%, 35%, and 45% LS. MATLAB-Simulink simulations provide the following sizes for both storage and photovoltaic systems of 38, 68, 87, 115, 153 kWh and 18, 18, 32, 43, 61 kWp, respectively. Results show the profitability of the proposed DR, with a payback period that ranges from 6 to 10 years and within 3 years when incentives are considered. NPV and IRR range from 34,261 to 101,428 € and from 7% to 15.9%, respectively. The amount of CO2 emissions is reduced up to 47.7% for the highest LS percentage. Finally, the investigated incentive program led to CO2 prices between 18.2 and 551.9 €/tonCO2. Finally, results from the stochastic analysis show a good consistence with deterministic results.

Efficient management of HVAC systems through coordinated operation of parallel chiller units: An economic predictive control approach

In developed countries, air conditioning systems have become major contributors to energy consumption in buildings. Cooling installations made up of independent chiller units connected in parallel pose a challenge in finding the most energy-efficient management strategy. This article proposes a novel economic model predictive control strategy to optimize the operation of multiple-chiller HVAC systems according to an economic cost comprising energy consumption as well as a thermal comfort index. Provided that the gradient of the economic cost function can be calculated or estimated, the proposed controller only entails the solution of a single quadratic programming (QP) problem at each sampling period, reducing computational requirements and thus facilitating the deployment on commercial embedded HVAC management platforms. The proposed controller improves economic performance. As our numerical analysis shows, optimal sequencing is achieved for the case of dual-chiller plants. Moreover, the controller adapts to possible variations in the economic criteria, enabling the system to respond to changes in electricity price and user preferences. A realistic case study, using a high fidelity model of a building simulated in TRNSYS, demonstrates the effectiveness of the proposed methodology. In particular, the proposed controller demonstrates to be more efficient than state-of-the-art QP-based economic predictive controllers in achieving a reduction of the energy consumption up to 5.19%, which is in line with the targeted reduction of 7.9% by 2030 in the EU Green Deal's ‘Fit for 55’ package.

Impact of battery electric vehicle usage on air quality in three Chinese first-tier cities.

China, the world leader in automobile production and sales, confronts the challenge of transportation emissions, which account for roughly 10% of its total carbon emissions. This study, utilizing real-world vehicle data from three major Chinese cities, assesses the impact of Battery Electric Vehicles (BEVs) on air quality. Our analysis reveals that BEVs, when replacing gasoline vehicles in their operational phase, significantly reduce emissions, with reductions ranging from 8.72 to 85.71kg of CO2 per vehicle monthly. The average monthly reduction rate is 9.47%, though this effect is less pronounced during winter. Advanced BEVs, characterized by higher efficiency and newer technology, exhibit greater emission reduction benefits. While private BEVs generally contribute positively to environmental outcomes, taxi BEVs, due to their intensive usage patterns, show less environmental advantage and may sometimes worsen air quality. Looking ahead, we project substantial emission reductions from the replacement of gasoline vehicles with electric alternatives over the next decade. Policymakers are urged to adopt proactive measures, focusing on promoting medium to large electric vehicles and fostering the use of private and ride-hailing electric vehicles.

A two-step smoothing Levenberg-Marquardt algorithm for real-time pricing in smart grid

<abstract><p>As is well known, the utility function is significant for solving the real-time pricing problem of smart grids. Based on a new utility function, the social welfare maximization model is considered in this paper. First, we transform the social welfare maximization model into a smooth system of equations using Krush-Kuhn-Tucker (KKT) conditions, then propose a two-step smoothing Levenberg-Marquardt method with global convergence, where an LM step and an approximate LM step are computed at every iteration. The local convergence of the algorithm is cubic under the local error bound condition, which is weaker than the nonsingularity. The simulation results show that, the algorithm can not only reduce the user's electricity consumption but also improve the total social welfare at the most time when compared with the fixed pricing method. Additionally, when different values of the approximating parameter are adopted in a smoothing quasi-Newton method, the price tends to that obtained by the present algorithm. Furthermore, the CPU time of the one-step smoothing Levenberg-Marquardt algorithm and the proposed algorithm are also listed.</p></abstract>

Development of an Integrated Solar-Biomass Energy System for Near-Zero Energy Buildings: A Technical and Environmental Study

The present study aims to satisfy the energy demands of a set of Norwegian residential structures with the least carbon dioxide and most renewable energy. Real-time data on building domestic hot water (DHW), heating, and electricity usage is used to plan and expand the renewable energy supply side. PVT panels provide DHW and electricity in the hybrid solar and biomass energy system. Heat is produced by the digester and heat pump. Also used is a twofold effect absorption refrigeration system for cooling. Rule-based regulation manages heat streams and redirects flows on the supply side. We provide the plant's size and execute the dynamic energy simulation. Electricity and biomass expenses determine building heating. The system is then tuned for operational conditions and compared to the design point. PVT may generate over 80% of annual DHW. Summertime radiation is more intense and can be turned into cooling energy, therefore 64.8% of cooling output comes from it. Digester/CC heats 66.55% of the structure, suggesting designers use biomass in winter due to increased energy costs. A parametric analysis shows that increasing PVT duration and tank size affects efficiency and emissions differently. Cost, efficiency, and emission index at TOPSIS are 9.73 $/hr, 36.8%, and 7.75 kg/MWh, according to optimization findings.

A Practical Implementation of RESTful API and WebSocket Protocol for Power Efficiency Monitoring: A Case Study at Muhammadiyah Orphanage

Power efficiency needs to be monitored to avoid adverse effects on electricity users, particularly those with significant inductive loads. However, current monitoring devices are limited in their ability to continuously record data and represent outputs effectively. This study designs an IoT-based reactive power and power factor monitoring system using a RESTful API for electrical equipment at a Muhammadiyah orphanage. The proposed system utilizes the PZEM-004T as the power sensor, the ESP32 microprocessor as the data processing unit, and a web server as the interface for settings and data visualization. Measurement records for reactive power and power factor can be stored in internal data storage. Additionally, remote monitoring can be conducted via WebSocket protocol and the Telegram application. The system has been tested by comparing reactive power, power factor, kWh, and cost with a standard power meter. The results demonstrate that the sensor exhibits an error of 9.02% in current measurement and 1.106% in active power measurement, with an average delay of 0.137 seconds via WebSocket-based server and 1.359 seconds using Telegram application.

Predicting tropospheric nitrogen dioxide column density in South African municipalities using socio-environmental variables and Multiscale Geographically Weighted Regression.

Atmospheric nitrogen dioxide (NO2) pollution is a major health and social challenge in South African induced mainly by fossil fuel combustions for power generation, transportation and domestic biomass burning for indoor activities. The pollution level is moderated by various environmental and social factors, yet previous studies made use of limited factors or focussed on only industrialised regions ignoring the contributions in large parts of the country. There is a need to assess how socio-environmenral factors, which inherently exhibit variations across space, influence the pollution levels in South Africa. This study therefore aimed to predict annual tropospheric NO2 column density using socio-environmental variables that are widely proven in the literature as sources and sinks of pollution. The environmental variables used to predict NO2 included remotely sensed Enhanced Vegetation Index (EVI), Land Surface Temperature and Aerosol Optical Depth (AOD) while the social data, which were obtained from national household surveys, included energy sources data, settlement patterns, gender and age statistics aggregated at municipality scale. The prediction was accomplished by applying the Multiscale Geographically Weighted Regression that fine-tunes the spatial scale of each variable when building geographically localised relationships. The model returned an overall R2 of 0.92, indicating good predicting performance and the significance of the socio-environmental variables in estimating NO2 in South Africa. From the environmental variables, AOD had the most influence in increasing NO2 pollution while vegetation represented by EVI had the opposite effect of reducing the pollution level. Among the social variables, household electricity and wood usage had the most significant contributions to pollution. Communal residential arrangements significantly reduced NO2, while informal settlements showed the opposite effect. The female proportion was the most important demographic variable in reducing NO2. Age groups had mixed effects on NO2 pollution, with the mid-age group (20-29) being the most important contributor to NO2 emission. The findings of the current study provide evidence that NO2 pollution is explained by socio-economic variables that vary widely across space. This can be achieved reliably using the MGWR approach that produces strong models suited to each locality.