Urban Electricity Research Articles

A Data-Driven Analysis Method for the Trajectory of Power Carbon Emission in the Urban Area.

"Industry 4.0" aims to build a highly versatile, individualized digital production model for goods and services. The carbon emission (CE) issue needs to be addressed by changing from centralized control to decentralized and enhanced control. Based on a solid CE monitoring, reporting, and verification system, it is necessary to study future power system CE dynamics simulation technology. In this article, a data-driven approach is proposed to analyzing the trajectory of urban electricity CEs based on empirical mode decomposition, which suggests combining macro-energy thinking and big data thinking by removing the barriers among power systems and related technological, economic, and environmental domains. Based on multisource heterogeneous mass data acquisition, effective secondary data can be extracted through the integration of statistical analysis, causal analysis, and behavior analysis, which can help construct a simulation environment supporting the dynamic interaction among mathematical models, multi-agents, and human participants.

T-LGBKS: An Interpretable Machine Learning Framework for Electricity Consumption Forecasting

Electricity is an essential resource that plays a vital role in modern society, and its demand has increased rapidly alongside industrialization. The accurate forecasting of a country’s electricity demand is crucial for economic development. A high-precision electricity forecasting framework can assist electricity system managers in predicting future demand and production more accurately, thereby effectively planning and scheduling electricity resources and improving the operational efficiency and reliability of the electricity system. To address this issue, this study proposed a hybrid forecasting framework called T-LGBKS, which incorporates TPE-LightGBM, k-nearest neighbor (KNN), and the Shapley additive explanation (SHAP) methods. The T-LGBKS framework was tested using Chinese provincial panel data from 2005 to 2021 and compared with seven other mainstream machine learning models. Our testing demonstrated that the proposed framework outperforms other models, with the highest accuracy (R2=0.9732). This study also analyzed the interpretability of this framework by introducing the SHAP method to reveal the relationship between municipal electricity consumption and socioeconomic characteristics (such as how changes in economic strength, traffic levels, and energy structure affect urban electricity demand). The findings of this study provide guidance for policymakers and assist decision makers in designing and implementing electricity management systems in China.

Ten questions concerning data-driven modelling and forecasting of operational energy demand at building and urban scale

Buildings account for over a third of end energy demand in many countries worldwide. Modelling this demand accurately marks the first step in producing forecasts that can help optimize energy efficiency as well as ensure stable grid operation, a growing concern with the electrification of heating and transportation demand and proliferation of renewable energy sources. These changes, for instance through large scale deployment of rooftop solar PV systems also mean that urban electricity demand is in a constant state of flux. To address these issues, a very large number of research papers have appeared in recent literature which propose or test different building and urban energy demand or load forecasting techniques, often with conflicting results. Meanwhile, in the same period, the broader field of forecasting has also witnessed tremendous development due to greater data availability, growth in computing power, and algorithmic innovations. As a result, there is little clarity in the energy demand forecasting field on best practices, especially in relation to the creation, evaluation, and utilization of these forecast models. This ten questions paper addresses this shortcoming and provides answers to the most important questions that researchers and practitioners alike need to ask themselves before creating their own demand forecaster, be it at the building or urban level. In addition to summarizing current best practices, the paper also provides an overview of some of the most important challenges related to energy demand forecasting and a principled framework to address them. Additionally, two companion notebooks provide an in-depth tutorial form introduction to load forecasting at both building and urban scale.

How climate change affects electricity consumption in Chinese cities-a differential perspective based on municipal monthly panel data.

Addressing the impacts of climate change has become a global public crisis and challenge. China is characterized by a complex and diverse topography and vast territory, which makes it worthwhile to explore the differential impacts of climate change on urban electricity consumption in different zones and economic development conditions. This study examines the differential impact of climate factors on urban electricity consumption in China based on monthly panel data for 282 prefectures from 2011 to 2019 and projects the potential demand for future urban electricity consumption under different climate change scenarios. The results show that (1) temperature changes significantly alter urban electricity consumption, with cooling degree days (CDD) and heating degree days (HDD) contributing positively to urban electricity consumption in areas with different regional and economic development statuses, with elasticity coefficients of 0.1015-0.1525 and 0.0029-0.0077, respectively. (2) The temperature-electricity relationship curve shows an irregular U-shape. Each additional day of extreme weather above 30 °C and below -12 °C increases urban electricity consumption by 0.52% and 1.52% in the north and by 2.67% and 1.32% in the south. Poor cities are significantly more sensitive to extremely low temperatures than rich cities. (3) Suppose the impacts of climate degradation on urban electricity consumption are not halted. In that case, the possible Shared Socioeconomic Pathways 1-1.9 (SSP1-1.9), SSP1-2.6, and SSP2-4.5 will increase China's urban electricity consumption by 1621.96 billion kWh, 2960.87 billion kWh, and 6145.65 billion kWh, respectively, by 2090. Finally, this study makes some policy recommendations and expectations for follow-up studies.

Utilizing Homer Power Optimization Software for A Techno-Economic Feasibility, Study of a Sustainable Grid-Connected Design for Urban Electricity in, Khartoum

HOMER (Hybrid Optimization of Multiple Electric Renewable) streamlines the design of distributed generation (DG) systems for a variety of grid-connected and off-grid applications. In Sudan, it is difficult to acquire an effective photovoltaic array for residential use due to a lack of energy consumption in power generation and access to technological, social, and environmental constraints. A model of a low-energy, solar-powered house that is suitable for Sudanese social and economic norms requires a high-quality architectural design. Method Using the HOMER software, the charge advantage analysis of a hybrid system was studied and assessed using the value for each kilowatt of grid-connected systems or utility grid. The simulation results have been presented as the most efficient and cost-effective method for achieving various home counts. At the current price, the hybrid system has a refund term of about fifty-four years. If turbine prices in Khartoum decline, the overall cost of energy will be reduced.

Prediction and comparison of urban electricity consumption based on grey system theory: A case study of 30 southern China cities

Rapid urbanization has put heavy pressure on urban energy supply over the past decades in China and such a trend is expected to continue. Therefore, it is critical to capture the impacts of key factors on urban energy consumption and further predict its future growth. This study proposes a framework to predict urban-scale electricity consumption which incorporates an original urban information database with 18 macroeconomic factors and 2 electricity consumption indicators. The grey prediction model is the core for prediction, whose performance is investigated in the established database. Meanwhile, multiple strategies, e.g., variable screening and optimal data allocation, have been conducted to improve the accuracy of the prediction. A case study of 30 cities in southern China indicates that the established database can well represent the relation between urban development and energy consumption. The proposed prediction framework performs well in forecasting urban-scale electricity consumption with less than 10 % of mean absolute percentage error, showing a positive correlation between model accuracy and data amount. Overall, the proposed prediction framework generates valuable insights into the interaction between urban development and electricity consumption, which can quantitatively inform urban designers when planning urban energy systems.

Impact of rural and urban electricity access and economic growth in Ghana: Does line transmission losses matter?

This paper aimed to estimate the impact of rural and urban electricity access on Ghana’s economic growth between 1993 and 2018 by controlling for electricity transmission losses as a percentage of total electricity transmitted. The study used Stock-Watson Dynamic Ordinary Least Square (DOLS) to estimate the impact of electricity access and transmission losses on Ghana’s economic growth. The Engle-Granger, Phillips-Ouliaris, Park’s added variables and Hansen parameter instability cointegration methods were applied to test the long-run cointegration of the studied variables The results show that a 1% increase in electricity access was associated with a 0.073% decrease in economic growth. Additionally, a percentage increase in the rural electricity access rate was associated with a 0.527% increase in national income per capita. In contrast, urban population electricity access had no statistically significant association with economic growth in Ghana. The study suggests that improving population access to electricity infrastructure services while reducing power wastage through electricity transmission loss minimization is crucial to achieving the long-term impact of electric power on socio-economic development and environmental sustainability in Ghana, subject to other equally essential conditions.

Impact of Reverse Power Flow on Distributed Transformers in a Solar-Photovoltaic-Integrated Low-Voltage Network

Modern low-voltage distribution systems necessitate solar photovoltaic (PV) penetration. One of the primary concerns with this grid-connected PV system is overloading due to reverse power flow, which degrades the life of distribution transformers. This study investigates transformer overload issues due to reverse power flow in a low-voltage network with high PV penetration. A simulation model of a real urban electricity company in Ghana is investigated against various PV penetration levels by load flows with ETAP software. The impact of reverse power flow on the radial network transformer loadings is examined for high PV penetrations. Using the least squares method, simulation results are modelled in Excel software. Transformer backflow limitations are determined by correlating operating loads with PV penetration. At high PV penetration, the models predict reverse power flow into the transformer. Interpolations from the correlation models show transformer backflow operating limits of 78.04 kVA and 24.77% at the threshold of reverse power flow. These limits correspond to a maximum PV penetration limit of 88.30%. In low-voltage networks with high PV penetration; therefore, planners should consider transformer overload limits caused by reverse power flow, which degrades transformer life. This helps select control schemes near substation transformers to limit reverse power flow.

Design of Inductive Power Taking System for Smart Grid Monitoring Equipment

Due to the continuous growth of urban electricity load and the increasing development of high-voltage transmission technology, the effective operation of the online monitoring equipment of the transmission line is particularly important, which is also a necessary condition for the development of the smart grid. However, the power supply conditions for the usage scenarios of smart grid monitoring equipment are limited. Conventional power supply methods such as battery power supply and new energy power generation have disadvantages such as cumbersome battery replacement, and the power supply stability is greatly affected by the environment, so they are difficult to meet the development trend of a rapid capacity increase of power grid and stable power supply of power equipment. Aiming at the technical bottleneck of smart grid monitoring equipment in power supply, this paper proposes a current transformer inductive power taking method based on capacitive reactance matching shunt, using the open air gap current transformer secondary winding set on the bus to connect the capacitor and its excitation inductance in series carry out capacitive impedance matching, and then shunt through another branch with load and connect it at both ends of the open air-gap current transformer to obtain electric energy. Through the circuit model experiment of the inductive power-taking system, it is verified that the current transformer inductive power-taking method based on capacitive reactance matching shunt can obtain more power.

Urban Electric Hybridization: Exploring the Politics of a Just Transition in the Western Cape (South Africa)

ABSTRACT Focusing on the adoption of rooftop solar photovoltaics (PV) by high-income households and businesses in the Western Cape, South Africa, the article analyzes its effects on the hybridization of urban electricity systems and the ability of municipalities to drive a just transition in cities where inequality remains very high. By reducing municipal electricity sales, decentralized solar technologies threaten the surpluses generated from charges paid by grid customers, which are essential to subsidize electricity services for the poor and support other municipal services. Based on fieldwork in four Western Cape cities, the paper shows that municipalities are implementing a variety of local arrangements (regulatory, tariff, and technical) to control distributed electricity generation and are seeking, with mixed success, to avoid a post-carbon transition model that undermines grid benefits by creating a new energy divide.

High-Resolution Carbon Accounting Framework for Urban Water Supply Systems.

Decarbonization of urban infrastructure systems is imperative to meeting global climate goals. Urban water supply systems (UWSSs) account for 1-3% of urban electricity consumption in the U.S., a value expected to increase, as municipalities tap nontraditional water supplies that are either more distant or require more energy-intensive treatment. Reducing the carbon intensity of UWSSs will require a combination of infrastructure upgrades, operational modifications, and behavioral interventions, but urban water planners, water treatment system operators, and consumers lack transparent tools for quantifying the carbon emission implications of these decisions. We propose a high-resolution carbon accounting framework that allows for attribution of carbon emissions to individual water sources, water system components, or individual consumers in a UWSS. The high temporal resolution of this framework also enables rapid assessment of the potential for operational and behavioral interventions to reduce the carbon intensity of UWSSs. We demonstrate this carbon accounting framework on a real-world UWSS serving a city of roughly 100 000 residents. The high spatial and temporal resolution, coupled with the scalability of this approach, makes it a valuable tool for consulting engineers, operators, and consumers seeking to deliver Net Zero water supplies.

Optimal energy and reserve management of a smart microgrid incorporating parking lot of electric vehicles/renewable sources/responsive-loads considering uncertain parameters

Due to the increasing presence of electric vehicles (EVs) in urban electricity distribution networks, distribution network operators face the challenge of energy management. A smart parking lot (SPL), renewable energy sources (RESs) such as photovoltaic systems (PV) and wind turbines (WT), and local dispatchable generators (LDG) such as microturbines (MT) and fuel cells (FC) are integrated as a microgrid (MG) while an energy management system is presented in this study which considers the uncertainties of wind speed, solar irradiation, and load consumption. The optimal operation of the SPL, which serves as a load and energy generation source for the distribution network, is done in this article with the goal of lowering costs. To cut costs, demand response program (DRP) based on a time of use (TOU) tariff is utilized, which moves a part of load from on-peak to off-peak time intervals, flattening the load curve. The goal is to reduce the operational expenses of the upstream grid (UG), LDGs, and SPL while taking into account the technical and physical limits of the units. Furthermore, for dealing with the uncertainties of load consumption and wind generation, this research employs a new uncertainty modeling method based on Hong's two-point estimate method. The suggested model is investigated by applying the General Algebraic Modeling System (GAMS) software and is formulated as mixed linear programming (MIP). The suggested model includes both a spinning reserve of LDG and a SPL, and the simulation results verified that the DRP has a good impact on lowering operation costs.

Research on the synergies between low-carbon pilot city policy and high-speed railways in improving Chinese urban electricity efficiency

PurposeGlobal climate change is a serious threat to the survival and development of mankind. Reducing carbon emissions and achieving carbon neutrality are the keys to reducing greenhouse gas emissions and promoting sustainable human development. For many countries, taking China as an example, the electric power sector is the main contributor to the country’s carbon emissions, as well as a key sector for reducing carbon emissions and achieving carbon neutrality. The low-carbon transition of the power sector is of great significance to the long-term low-carbon development of the economy. Therefore, on the one hand, it is necessary to improve the energy supply structure on the supply side and increase the proportion of new energy in the total power supply. On the other hand, it is necessary to improve energy utilization efficiency on the demand side and control the total primary energy consumption by improving energy efficiency, which is the most direct and effective way to reduce emissions. Improving the utilization efficiency of electric energy and realizing the low-carbon transition of the electric power industry requires synergies between the government and the market. The purpose of this study is to investigate the individual and synergistic effects of China’s low-carbon policy and the opening of urban high-speed railways (HSRs) on the urban electricity consumption efficiency, measured as electricity consumption per unit of gross domestic product (GDP).Design/methodology/approachThis study uses a panel of 289 Chinese prefecture-level cities from the years 1999–2019 as the sample and uses the time-varying difference-in-difference method to test the relationship between HSR, low-carbon pilot cities and urban electricity consumption efficiency. In addition, the instrumental variable method is adopted to make a robustness check.FindingsEmpirical results show that the low-carbon pilot policy and the HSR operation in cities would reduce the energy consumption per unit of GDP, and synergies occur in both HSR operated and low-carbon pilot cities.Research limitations/implicationsThis study has limitations that would provide possible starting points for future studies. The first limitation is the choice of the proxy variable of government and market factors. The second limitation is that the existing data is only about whether the high-speed rail is opened or not and whether it is a low-carbon pilot city, and there is no more informative data to combine the two aspects.Practical implicationsThe findings of this study can inform policymakers and regulators about the effects of low-carbon pilot city policies. In addition, the government should consider market-level factors in addition to policy factors. Only by combining various influencing factors can the efficient use of energy be more effectively achieved so as to achieve the goal of carbon neutrality.Social implicationsFrom the social perspective, the findings indicate that improving energy utilization is dependent on the joint efforts of the government and market.Originality/valueThe study provides quantitative evidence to assess the synergic effect between government and the market in the low-carbon transition of the electric power industry. Particularly, to the best of the authors’ knowledge, it is the first to comprehend the role of the city low-carbon pilot policy and the construction of HSR in improving electricity efficiency.

Logistic and scheduling optimization of the mobilized and distributed battery in urban energy systems

Building a new power system with renewable energy as the main body is the only way to solve the problem of global climate change and achieve the dual carbon goal. However, the increasing penetration rate of renewable energy and large-scale electric vehicles have brought great challenges to the safe and reliable operation of traditional power system, resulting in the inability to effectively meet the enormous urban electricity demand in real time. In response, this paper proposes to adopt mobilized and distributed batteries and establishes a two-stage logistic and scheduling optimization model, thereby realizing the power transportation between renewable energy power plants and cities. The first stage model aims to optimize the battery transportation and logistics scheme with minimizing the total battery procurement and transportation cost as the objective function, considering various constraints including railway transport capacity limitation, battery supply and demand balance, and other technologies. The second stage model aims to maximize the peak shaving effect of urban power system and optimize the railway departure time with mobile batteries, considering the train operation, battery supply limitation, and battery transportation balance constraints. Finally, a quantitative economic evaluation of six cities (Heilongjiang, Gansu, Fujian, Shanghai, Jilin, and Liaoning) in China is presented, and the results indicate that the average levelized cost of electricity is as low as 0.052 $/kWh, illustrating the great technical and economic performance of battery logistics and scheduling. In addition, the impact of various sensitive factors on the economic performance of the urban energy system is implemented.

An Assessment of Electric Power Consumption Using Random Forest and Transferable Deep Model with Multi-Source Data

Reliable and fine-resolution electric power consumption (EPC) is essential for effective urban electricity allocation and planning. Currently, EPC data exists mainly as statistics with low resolution. Many studies estimate fine-resolution EPC based on the positive correction between stable nighttime light and EPC distribution. However, EPC is related to various factors other than nighttime light and is spatially non-stationary. Yet this has been ignored in current research. This study developed a novel method to estimate EPC at 500 m resolution by considering spatially non-stationary through fusing geospatial data and high-resolution satellite images. Deep transfer learning and statistical methods were used to extract socio-economic, population density, and landscape features to describe EPC distribution from multi-source geospatial data. Finally, a random forest regression (RFR) model with features and EPC statistics is established to estimate fine-resolution EPC. A study area of Shenzhen city, China, is employed to evaluate the proposed method. The R2 between predicted EPC and statistical EPC is 0.82 at sub-district level in 2013, which is higher than an existing EPC product (Shi’s product) with R2=0.46, illustrating the effectiveness of the proposed method. Moreover, the EPC distribution for Shenzhen from 2013 to 2019 was estimated. Furthermore, the spatiotemporal dynamic of EPC was analyzed at the pixel and sub-district levels.

Non-payment culture and the financial performance of urban electricity utilities in South Africa

ABSTRACT Non-payment for services continues to challenge sustainability in municipal service delivery across South Africa. Literature provides that the culture of non-payment stems from the apartheid era where mass civil disobedience manifested through boycotting the payment of rates. This study examines the impact of the non-payment culture on municipal financial performance in South Africa. Panel data for 28 municipalities for the years 2005–19 is used, and the random-effects model is employed to estimate the relationship between municipal financial performance and non-payment. Results confirm that non-payment has a negative impact on financial performance. For every R1000 increase in bad debts written off, financial performance is reduced by R291. Further, grants from the national government, the number of consumers, and the number of household units receiving free basic electricity positively affect financial performance. These revelations warrant the need for more innovative approaches that transform non-payment into a culture of payment.

Co-production of access and hybridisation of configurations: a socio-technical approach to urban electricity in Cotonou and Ibadan

ABSTRACT The article examines the dynamics of access to electricity in two West African cities: Cotonou (Benin) and Ibadan (Nigeria). Due to poor supply from the grid, households are developing varied ways of accessing electricity, based on different socio-technical dispositifs. In this paper we first demonstrate that access to electricity is based on co-production processes that must be approached from a multi-scale perspective (from the household to the urban scale). We then argue that particular attention to the socio-technical and spatial dimension of co-production arrangements makes it possible to interpret urban electrical configurations and their evolution. We thus show that co-production processes, relying on many actors and technologies to meet a growing and diversified demand for electricity in cities, support an ongoing movement of extension-hybridisation of electricity configurations on an urban scale, thus offering an interesting perspective on power changes in sub-Saharan Africa.

Electrifying urban Africa: energy access, city-making and globalisation in Nigeria and Benin

Electricity access has become a crucial issue in global South cities. While demand is growing, conventional grids are failing or insufficient, especially in Africa. Urban dwellers therefore have to develop a wide range of (in)formal infrastructures to meet their daily electricity needs. Building on recent studies on urban electricity in the global South, this paper aims to contribute to the debates on hybrid forms of electricity provision by analysing the diffusion of solar panels and generators in two cities, Ibadan in Nigeria and Cotonou in Benin. Although neighbouring and relatively similar, these two cities illustrate distinct daily electrical lives. In Nigeria, an electricity-exporting country, people face daily power outages. In Benin, a country that depends on Nigeria for its supply, there is electricity but it is difficult to connect to the grid because of connection costs. Based on an empirical study, the article shows that Ibadan’s inhabitants use generators as a complement to a conventional grid that is almost universal but unreliable. In Cotonou, solar energy is an alternative until they can connect to the grid. Generators and solar panels have become the material markers of urban Africa, providing information on inequalities in access to electricity.

Recent Trends in Urban Electricity Consumption for Cooling in West and Central African Countries

Recent Trends in Urban Electricity Consumption for Cooling in West and Central African Countries

Multi-objective optimization and exergoeconomic evaluation of a hybrid geothermal-PVT system integrated with PCM

The main purpose of this research is to study the techno-economic analysis of a photovoltaic thermal collector (PVT) combined with phase change materials (PCM) and ground source heat pump (GSHP). In the first step of this study, a numerical transient simulation of a water-to-water GSHP was developed by using a vertical U-type ground source heat exchanger (GSHX) and variable speed drive (VSD) compressor in the multi-usage operation modes (heating and cooling). To achieve the most supreme system performance, the multi-objective optimization of the Levelized cost of energy (LCOE) and exergy efficiency was done by using the multi-objective non-dominated sorting genetic algorithm version || (NSGA-||). This algorithm considers substantial elements such as the number of boreholes, depth of drilling for the location of pipes in GSHX, setpoint temperature of fan coil, length of converters, the number of PVT panels, slope and direction of PVT panels, the number of batteries, volume of the storage tank of phase change materials and percentage of phase change materials in the tank. The simulation of the heat pump is accomplished through a numeral coding in the Engineering Equation Solver (EES) and also for the optimization part, a programming method used in the MATLAB program. Moreover, due to the evaluation of the transient response of the model, TRNSYS software was implemented. Based on the research achievements, the Single objective optimization of exergy efficiency for the combined PVT-PCM and GSHP systems can be conducted to the highest solar factor (SF) and the lowest amount of urban electricity consumption. Furthermore, the calculation of the annual required load of the building for different scenarios has shown that the use of collectors in this combined system has reduced the total load of the building by 6.5%. The maximum percentage of PVT energy efficiency belongs to the first scenario with a value of 53% and the lowest one with a difference of 43% is for the fourth scenario.