Domestic Electricity Research Articles

Methodological Principles of Planning the Development of Mongolia’s Electric Power System

The article addresses matters concerned with the methodological features of planning the development of the electric power system (EPS) of Mongolia, the concepts laid down in the documents defining the development of Mongolia’s energy sector, and some results of studies on working out the country’s EPS development model. The EPS management and planning problems, which follow from the need to improve the energy security indicators and matching it with the growth of domestic electricity consumption and the features of the external market, have been defined, and possible options for a unified electric power system have been proposed based on the systems analysis method in comparison with the mathematical modeling method.

Investigation of individual motives and decision paths on residential energy supply systems

A better understanding of end users’ motives for choosing their energy supply system (heating and domestic hot water, cooling and electricity) can support the establishment of favorable conditions for the energy transition. In this research project, a survey was conducted in the Austrian residential sector to identify end users' interests and decisions for certain energy supply systems as well as motives for the choice. Based on 169 responses to the questionnaire, a statistical analysis was performed to evaluate the influence of gender aspects on interests and decisions. More than 90% of respondents required robust and efficient energy supply systems, which should have the highest technical standards. The environmental performance was also highly valued, whereas financial aspects, including investment costs were considered less important. 79% of men were mainly involved in the decision-making process, whereas only 59% of women were involved and, in most cases, made the decision together with their partner (52%). Identifying these motives and analyzing investment decisions enables the future integration of social and gender aspects into optimization models for individual households or energy communities.

A Digital Twin Approach to City Block Renovation Using RES Technologies

The building sector accounts for over 40% of global energy consumption, and many buildings are old and inefficient. However, the current pace of building renovation is not sufficient to make a tangible impact. A new strategy is needed to accelerate the renovation process. Renovation at the district level and the use of digital tools, such as a digital twin (DT) of a city district, can provide a solution. This paper proposes a novel approach to city block renovation using renewable energy sources (RES), including photovoltaic (PV) solar panels, heat pumps (HP), and electric heaters (EH), while utilizing a DT of a city district to provide a user-friendly representation of the results and data needed for holistic solutions. The proposed method combines an optimization model of the optimal heating system with a solar PV simulation technique to analyse hybrid RES solutions and potential on-site energy generation and supply. Several scenarios are simulated to evaluate RES solutions in the renovation process of the city block using the DT concept. The simulation results demonstrate that a hybrid RES solution, which includes a PV system and a heating system, is optimal when the on-site generated energy is used not only for domestic electricity consumption, but also for the operation of HPs and EHs for heat generation. This study highlights the importance and significance of a DT approach to city block renovation and provides a new solution to accelerate the renovation process and reduce energy consumption in the building sector.

Energy and stress analysis of a hybrid photovoltaic thermal module

Photovoltaic (PV) solar panel systems are widely used for meeting domestic electricity requirements because of their simple and cost-effective operation. It is well known that the electrical efficiency of PV panels is abruptly disturbed by cell temperature which depends upon several factors, including ambient temperature. In this study, the increased electrical efficiency of a PV module has been demonstrated by integrating a water storage tank at the backside of the PV module turning the simple PV module into hybrid Photovoltaic Thermal (PVT) module. The water tank acts as the reservoir and provides water flow at the backside of the PV module and the temperature difference between water and cell causes the heat exchange between the back surface of the PV panel and water, resulting in the lowering of the PV module cell temperature. This hybrid design has resulted in a 19% increment in the electrical efficiency of the PV panel. The system is designed to store hot water at 40 °C. In addition, a PVT thermal efficiency of 36.5% is also achieved which can be used to meet hot water domestic applications. The structural analysis of the system design through ANSYS® is also carried out to determine the effects of water pressure on the tank and it has been found that the design is safe and secure.

A hybrid machine learning approach for forecasting residential electricity consumption: A case study in Singapore

Ensuring effective forecasting of buildings' energy consumption is crucial in establishing a greater understanding and improvement of buildings' energy efficiency. In Singapore, domestic electricity usage in public residential buildings takes up a significant portion of the country's annual energy consumption. Having effective forecasting approaches is thus important in supporting relevant strategies and policy making. In this research, we proposed a hybrid approach that was based on a combination of building characteristics and urban landscape variables to predict residential housing electricity usage in Singapore. XGboost was also incorporated inside the hybrid approach as the preferred machine learning approach for energy consumption predictions. To demonstrate our proposed approach's predictive strength, the performance of our proposed hybrid machine learning approach was compared with two other models, Geographically Weighted Regression (GWR) model and the Random Forest (RF) model. Results showed that our proposed hybrid model had outperformed these abovementioned approaches with higher accuracy (r2 value of 0.9). The proposed approach had thus been effective in forecasting electricity consumption for public housing in Singapore, and it could also be utilised in other similar urban areas for future electricity consumption forecasting.

Which industrial path contributed to CO2 transfer and emissions slowing-down of China and the United States most in international trade?

Carbon transfer in international trade has triggered disputes on environmental equity between developed and developing countries. Hence, it is urgent to make it clear the carbon transfer process of international trade between China and the United States, explore the adjustment plan of emission reduction responsibility and formulate targeted emission reduction measures. These will play an important leading and demonstration effect in addressing global warming and coordinating international carbon reduction. This study built a CO2 international trade transfer network model of China and the United States. Based on identifying the carbon transfer path flowing from primary product sector to final production sector, and then to final consumption, we analyzed the industrial association relationship and distribution differences between China and the United States with external countries and regions. Lastly, we put forward emission reduction policy implication in industrial scale. The key paths of China and the United States are concentrated in the two export and import transfer types respectively. Among the key paths of China's bilateral primary production export type, paths from domestic Electricity, Gas and Water to foreign Construction accounted for the largest 40% share. As for the United States, transfer type of bilateral final production import is the most prominent, and path number and flow from foreign Transport to domestic Public Administration both accounted for more than 30%. Based on shared responsibility to adjust the emission reduction tasks under the Paris Agreement, China and the US will allow to emit 10% more and reduce 6% more, reaching 54% and 25% respectively. Taking into full consideration the national economic development level and the differences in the production technology level of industrial sectors, as well as the upstream and downstream related sectors' relationship, the refined emission reduction strategy is proposed from different aspects, such as technical assistance, financial support, import control, technology sharing and consumption transformation.

Evaluation of surplus hydroelectricity potential in Nepal until 2040 and its use for hydrogen production via electrolysis

The abundant hydro resources in Nepal have resulted in the generation of electricity almost exclusively from hydropower plants. Several hydropower plants are also currently under construction. There is no doubt that the surplus electricity will be significantly high in the coming years. Given the previous trend in electricity consumption, it will be a challenge to maximize the use of surplus electricity. In this work, the potential solutions to maximize the use of this surplus electricity have been analysed. Three approached are proposed: (i) increasing domestic electricity consumption by shifting the other energy use sectors to electricity, (ii) cross-border export of electricity, and (iii) conversion of electricity to hydrogen via electrolysis. The current state of energy demand and supply patterns in the country are presented. Future monthly demand forecasts and surplus electricity projections have been made. The hydrogen that can be produced with the surplus electricity via electrolysis is determined and an economic assessment is carried out for the produced hydrogen. The analysis of levelized cost of hydrogen (LCOH) under different scenarios resulted values ranging from 3.8 €/kg to 4.5 €/kg.

A pattern changes evaluation on household greenhouses gases during pandemic in Surakarta

The pandemic has caused a change in activity patterns that affect energy consumption and waste production due to restriction of activities. The purpose of this study is to find the changes in household GHG emission pattern during the pandemic in Surakarta Municipality. The focus of household GHG calculated in the study is electricity and liquid petroleum gas (LPG) consumption, waste production, and household batik industry. Greenhouse gases calculated with IPCC Guideline. Data activity used reference based approach. The total of household activity emission during the pandemic in Surakarta reaches 1035680.40 tonnes CO2eq, contributing to 33.5% of the total emission. The number comes from electricity consumption that produced 900709.38 tonnes CO2eq/year, LPG consumption that produced 107494.09 tonnes CO2eq/year, waste that produced 26050.44 tonnes CO2eq/year, and household batik industry that produced 1427.55 tonnes CO2eq/year. Monthly electricity consumption had 10% increase during the pandemic, surpassing the pre-pandemic highest value of 5.71%. LPG consumption had a decrease of 4.86%. Domestic waste production has an increasing trend of 36%, mostly containing inorganic waste (plastic). Pandemic created a new pattern on domestic electricity consumption.

Sustainability of building-integrated bioclimatic design strategies depending on energy affordability

Bioclimatic design strategies have proven their efficiency in improving the sustainability of buildings. However, their economic benefits, regarded as the main incentive for building owners to incorporate such strategies into buildings, are still unclear since they depend on several factors including the countries' energy accessibility level. This study explores the role of energy affordability level and climatic conditions in incorporating bioclimatic design strategies. Therefore, the effectiveness of integrating seven bioclimatic approaches into a common residential building in six different locations worldwide with disparate climatic conditions, gross domestic products and electricity prices is assessed. The outcomes of each strategy are generated using EnergyPlus software and evaluated based on key energy, economic and environmental metrics. The findings reveal that, for most bioclimatic design strategies, the highest economic profits are achieved in regions where the electricity price to gross domestic product (EPGDP) ratio is high (i.e., where energy is unaffordable). Indeed, the shortest discounted payback period is obtained by using suitable glazing size in hot/moderate climates with higher EPGDP ratio (0.04/0.13 years) and thermal insulation in cold ones (1.17 years). Moreover, the greatest net present values are acquired by integrating thermal insulation in hot/cold zones with up to k$14.04/k$76.73, and window opening design in moderate climates (k$7.32). Additionally, the investigated strategies exhibit significant energy savings and carbon emissions mitigation. This study confirms the benefits of bioclimatic design; nevertheless, awareness campaigns and incentive programs are also essential in motivating households to integrate sustainable strategies into their buildings, especially in regions where energy is affordable.

Powering Ghana’s future: unraveling the dynamics of electricity generation and the path to sustainable energy

This study investigates powering Ghana’s future: unraveling the dynamics of electricity generation and the path to sustainable energy by estimating endogenous parameters and employing an unrestricted Vector Autoregression (VAR) model. The model examines the linear lead–lag relationships between variables in the Ghanaian electricity sector and power consumption, using data from 2002 to 2021. The results reveal structural long-and-short-run headwinds for the unrestricted models and indicate that the growth rate of the Gross Domestic Product (GDP) and electricity from fossil fuels are directly correlated. Granger causality analysis highlights a feedback relationship between GDP growth rate and electricity from fossil fuel sources. The impulse response function reveals that the GDP growth rate is sensitive to exogenous shocks with lasting effects. Variance decomposition results show that renewable energy without hydropower explains a minimal variance due to shocks, while total global greenhouse emissions account for a significant proportion of the variance due to headwinds. Electricity from fossil fuel sources explains a substantial part of the variance due to headwinds, suggesting Ghana’s overreliance on conventional energy sources. The study forecasts that installed renewable energy capacity will experience considerable growth by 2036, accounting for most of the energy mix. To promote a sustainable energy future, the study recommends implementing fiscal instruments that incentivize renewable energy consumption, gradually diversifying the energy mix towards natural gas as a medium-term transition fuel for grid electricity generation and shifting entirely to renewables in the long time. This research contributes valuable insights into the dynamics of electricity generation in Ghana and provides policy recommendations for sustainable energy development.

Economic incentives for capacity reductions on interconnectors in the day-ahead market

We consider a zonal international power market and investigate potential economic incentives for short-term reductions of transmission capacities on existing interconnectors by the responsible transmission system operators (TSOs). We show that if a TSO aims to maximize domestic total welfare, it often has an incentive to reduce the capacity on the interconnectors to neighboring countries. In contrast with the (limited) literature on this subject, which focuses on incentives through the avoidance of future balancing costs, we show that incentives can exist even if one ignores balancing and focuses solely on welfare gains in the day-ahead market itself. Our analysis consists of two parts. In the first part, we develop an analytical framework that explains why these incentives exist. In particular, we distinguish two mechanisms: one based on price differences with neighboring countries and one based on the domestic electricity price. In the second part, we perform numerical experiments using a model of the Northern-European power system, focusing on the Danish TSO. In 97% of the historical hours tested, we indeed observe economic incentives for capacity reductions, leading to significant welfare gains for Denmark and welfare losses for the system as a whole. We show that the potential for welfare gains greatly depends on the ability of the TSO to adapt interconnector capacities to short-term market conditions. Finally, we explore the extent to which the recently introduced European “70%-rule” can mitigate the incentives for capacity reductions and their welfare effects.

Machine Learning for Geothermal Resource Exploration in the Tularosa Basin, New Mexico

Geothermal energy is considered an essential renewable resource to generate flexible electricity. Geothermal resource assessments conducted by the U.S. Geological Survey showed that the southwestern basins in the U.S. have a significant geothermal potential for meeting domestic electricity demand. Within these southwestern basins, play fairway analysis (PFA), funded by the U.S. Department of Energy’s (DOE) Geothermal Technologies Office, identified that the Tularosa Basin in New Mexico has significant geothermal potential. This short communication paper presents a machine learning (ML) methodology for curating and analyzing the PFA data from the DOE’s geothermal data repository. The proposed approach to identify potential geothermal sites in the Tularosa Basin is based on an unsupervised ML method called non-negative matrix factorization with custom k-means clustering. This methodology is available in our open-source ML framework, GeoThermalCloud (GTC). Using this GTC framework, we discover prospective geothermal locations and find key parameters defining these prospects. Our ML analysis found that these prospects are consistent with the existing Tularosa Basin’s PFA studies. This instills confidence in our GTC framework to accelerate geothermal exploration and resource development, which is generally time-consuming.

Experimental validation of a solar system based on hybrid photovoltaic-thermal collectors and a reversible heat pump for the energy provision in non-residential buildings

This work aims to validate a transient model of a solar hybrid pilot plant based on photovoltaic-thermal (PV-T) collectors integrated via thermal storage tanks with an air-to-water reversible heat pump (rev-HP). The pilot plant is in operation and provides space heating, cooling, domestic hot water (DHW) and electricity to an industrial building located in Zaragoza (Spain). The plant consists of eight uncovered PV-T collectors (2.6 kWe, 13.6 m2), two water tanks and a rev-HP with a nominal thermal power of 16 kW for heating and 10.5 kW for cooling. The validation results show that the transient model fits the experimental performance of the PV-T collectors, with an average error of -16% and 3%, for the thermal and electrical generation respectively. The accuracy of the estimated rev-HP performance depends on the operation mode. The estimated COP in cooling mode has an average error of 14%, while in heating mode has an average error of -10%. The results show that the integration of the thermal and electrical generation of the PV-T collectors with a high-performance rev-HP allows the solar PV-T system to be self-sufficient to satisfy the building energy demand.

Research on the factors influencing the growth of residential electricity consumption

Influenced by the continuous economic and social development and frequent occurrence of extreme weather, the factors influencing the growth of residential domestic electricity consumption in China are increasing, the mechanism of influence is more complex and the trend judgment is more difficult to judge. To this end, this paper studies the influence mechanism of residential domestic electricity consumption growth, researches the driving force of residential domestic electricity consumption growth, carries out correlation and regression analysis of factors influencing residential domestic electricity consumption growth, and draws the following conclusions: the number of air conditioners per 100 households has the strongest influence on per capita residential domestic electricity consumption, followed by the influence of per capita disposable income, and the influence of urbanization rate and population is relatively weak.

Investigation of Domestic Wastewater Treatment and Electricity Generation Using A Two Chambered Microbial Fuel Cell with Composite Anode Electrode

Microbial fuel cell is a bioelectrochemical system that generate electricity with the oxidation of organic substrates by exoelectrogenic microorganisms. It can be said that the studies on microbial fuel cells (MFCs) are generally aimed to increase the amount of energy produced. In this study, domestic wastewater treatment was investigated by using a two-chamber microbial fuel cell. Ankara Tatlar Wastewater Treatment Plant influent water was used as substrate. In order to reduce the diffusional resistance by reducing the distance between the anode and cathode electrode, the cathode chamber was placed in the middle of the anode chamber. Anode and cathode chambers separated by Nafion 117 membrane. In addition, an anode consisting of a stainless-steel mesh and a graphite supported catalyst has been developed to increase the electricity generation potential. During the experiment, the maximum voltage and the maximum power density values were obtained as 595 mV and 205.867 mW/m2 respectively. COD value is a criteria which indicates the waste treatment ability of the systems. For this experiment COD values of the wastewaster were measured both the beginning and the end of the experiment as 451 mg/L O2 and 361 mg/L O2. These results proved that the developed electrode structure is at a comparable level with the values reported in the literature for two-chamber MFC studies.

Design and Management of Electricity Marketing Information System Based on Multisensor Fusion

The electricity marketing management information system realizes the whole process of management from accepting customers’ electricity application to electricity bill recovery and from business processing to decision support. The electricity marketing management information system is a modern management tool for electric electricity enterprises to go to the market, expand the market, increase supply, expand sales, and provide customers with high-quality services. This paper has completed the following work: (1) described the development of domestic and foreign electricity marketing management information system and provided a theoretical basis for the design scheme proposed later. (2) The construction of the marketing system should focus on eight key points, establish a service-oriented architecture, plan the hardware platform in combination with the principles of advanced and practicality, and at the same time, apply multisensors to the marketing technology support system to improve the efficiency of data interaction. (3) According to the characteristics of the application software of the electricity marketing management information system, the software that runs between the components of the distributed, enterprise-wide C/S application or within the components is adopted. The performance of the software and hardware of the system is tested experimentally, and the results show that the electricity marketing information system has good performance.

Urban domestic electricity consumption in relation to households’ lifestyles and energy behaviours in Burkina Faso: Findings from a large-scale, city-wide household survey

Information on actual domestic electricity use in Burkina Faso, where the urban zones and residential sector account for 74% and 33% respectively of the total electricity use, remains very challenging to find, as little research currently exists. This study aimed therefore, to provide the first ever insights into the actual urban residential electricity use. A survey with 387 households, the first large-scale, city-wide household electricity study undertaken in Burkina Faso to the authors’ knowledge, was conducted in the city of Ouagadougou. Information on the households' characteristics and behaviours were collected to yield a first comprehensive analysis on the actual city-scale domestic electricity use. Findings demonstrated an average electricity use of 2395 kWh/year by households. Cooling accounts for almost 40% of the total domestic electricity use, followed mainly by cooking and food preserving (23%) and information-communication-entertainment (19%) activities. Three groups of consumers were then formed based on their electricity use level, to investigate interactions between electricity use and households’ lifestyles. The study’s findings lay therefore, the foundations for a better understanding of actual urban domestic electricity use patterns and could help to develop more suitable policies and actions targeting energy conservation in the residential sector.

Climatic and different human influences on annual and seasonal streamflow with considering the soil water storage change in the middle reaches of the Yellow River basin, China

Recent climate change and more intensive human activities have induced significant changes in streamflow along the middle reaches of the Yellow River basin (MRYRB), which has attracted considerable attention from the scientific community. However, most previous studies have focused on changes in annual streamflow by examining data from a small number of hydrological stations and the role of the various human activities remains unclear. Here we used an extended Budyko-based framework that considered changes in soil water storage (ΔS) to determine the contributions of changes in streamflow, both at the annual and seasonal scale. These analysis were conducted on 40 catchments and three sections (Toudaoguai–Longmen, TDG–LM; Longmen-Huayuankou, LM–HYK; and Toudaoguai–Huayuankou, TDG–HYK) in the MRYRB from 1971 to 2015. Results showed that streamflow at most catchments and sections declined dramatically after years of abrupt change detected by the Pettitt test, with decreases occurring at a faster rate during flood seasons (42.9%) than during non-flood seasons (31.8%). Although human behaviors were often the main cause of streamflow reductions at the annual and seasonal scales, the effects of ΔS became more important at increasingly finer scales. Indeed, they were of equal or greater importance than human impacts in some catchments, with corresponding relative contributions up to 48%. The effects of human activities may possibly have been overestimated or underestimated by 1–16% in three sections when the impact of ΔS was not considered. Further analysis indicated that human water use – particularly the rapid increase in water withdrawals for domestic purposes, electricity generation, and manufacturing – was the primary factor among human activities that affected streamflow reduction in the LM–HYK and TDG–HYK sections at different timescales, comprising 60–90% of all impacts. By contrast, the effects of water use and other human factors were almost equal in the TDG–LM section. Among the other human factors, vegetation restoration played a more critical role in the TDG–LM section, whereas the construction of check-dams was more important in the LM–HYK section. These findings of our study can enhance the understanding of hydrological changes in response to environmental factors.

PREDICTION OF CARBON DIOXIDE EMISSION FROM ELECTRICITY GENERATION IN ASEAN

The Coal-Fired Power Plants with low-emissions plants in the power sector can avoid nearly 800 million tonnes CO2 by 2030. The number of populations, Gross Domestic Product (GDP), electricity generation, NDC, and relevant energy policies influenced the prediction of Carbon Dioxide (CO2) emission from electricity generation. Aim: The objective is to analyze trends of CO2 growth related to electricity generation activities from countries in ASEAN with an emphasis on identifying the factors that influence it and evaluating the effectiveness of existing decarbonization policies in AMS and developing suitable strategies to reduce future CO2 emissions. Methodology and Result: Predicting CO2 emissions is conducted using three approaches by considering the newest NDC and energy policies, conducted by descriptive and quantitative analysis based on secondary data of (1) general information on ASEAN Member States (AMS), (2) information on power plants, (3) information on fuel use in electricity generation, and (4) decarbonization policies in AMS. Results indicated that the availability of potential energy sources (coal/oil/gas/RE) influences the dominant energy used in AMS Prediction of CO2e emission in the 2020 to 2040 period identified Vietnam as one of the highest CO2e emitters (380,1 Mt CO2e) in the electricity sector by 2030. Conclusion, significance and impact study: AMS needs to make a more aggressive energy policy to reduce CO2 level significantly, achieving the 2050 net zero transition targets. Ultimately, the shifting from coal-fired electricity plants to other fuel sources with a lower emission factor proves to be the most significant factor towards emission reduction.

Impacts of Tariffs on Energy Conscious Behavior with Respect to Household Attributes in Saudi Arabia

Historically, the combination of generous subsidies along with extreme climate has led to unsustainable domestic electricity consumption in Saudi Arabia. The residential sector constitutes a significant portion of this consumption. Amid the economic challenges, the country enforced a new electricity tariff for residential consumers in 2018. This study thus leverages change in 2018–2020 by collecting and analyzing the electricity consumption data of 73 households in the Eastern Province of Saudi Arabia. The energy consumption is modeled based on the households’ attributes (e.g., dwelling type, ownership, number of residents, rooms, ventilation type, etc.) and applied tariffs using a machine learning technique. The extreme learning machine (ELM) is employed in solving the overfitting problem due to low-volume data. The correlation matrix is also constructed to determine the relationship between the household attributes. The ELM model developed in this study extracts the correlation between the input variables in determining energy consumption and also predicts the energy consumption related to low consumption data. The findings indicated that the electricity consumption between the pre-revised tariff year and the revised tariff enforcement year saw a reduction which was consistent in the subsequent years. This was also validated by the paired sample t-test, which showed a significant decrease in electricity consumption for the study period. The analysis also revealed that several household attributes had a relatively high impact on the reduction in the electricity consumption level following the revised tariffs, whereas the majority of the attributes had a moderate impact. In addition to these key findings, the demonstrated pathway adopted in this study is itself a methodological contribution that provides critical information about the sensitivity of the impacts of tariffs on energy consumption with respect to different household attributes. Economic factors being the critical stress need to be blended with existing energy consciousness for positive changes in favor of energy-saving behavior of the household members. The study does not attempt to represent the population of concern, but demonstrates a methodology that would help unleash inherent energy consciousness in favor of sustainable and energy-efficient behavior.