Search Results (39)

The electric power industry is not only facing the pressure from the reduction of industrial and commercial electricity prices to stimulate the significant growth of demand, but also facing the increasingly serious pressure of unreasonable consumption caused by cross-subsidies; the cross-subsidy mitigation effect and energy-saving effect of the current tiered electricity price policy have basically disappeared. This article examines the main variables that affect the electricity demand and carbon emissions in order to develop a better tiered electricity pricing scheme that can effectively manage cross-subsidies in electricity prices while simultaneously saving energy and lowering carbon emissions. The China Statistical Yearbook’s electricity balance sheets for 2013–2020 are used in this article, along with pertinent data from the State Grid Corporation of China and the China Statistical Yearbook for 2006–2016. It builds an electricity demand model for classified users by using the time series analysis method and multiple statistical regression method. The variables are then subjected to a Granger causality test and a cointegration test in this article. The analysis shows that the adjustment of the electricity price policy has a significant impact on energy-saving and carbon reduction, and for residential electricity consumption, the income variable is the main factor affecting the electricity demand. We take residents’ affordability as the constraint condition for dividing the range of electricity and determining the beneficiary group, take the carbon emission responsibility target and the cross-subsidy degree alleviation target as constraints in determining the tiered price difference, propose an improvement scheme for the tiered electricity price, and carry out the sensitivity analysis of the influence between the parameters. The results show that the optimization improves the precision of the cross-subsidy treatment and significantly improves the effects of energy conservation and emission reduction. Full article

Power system stability (PSS) refers to the capacity of an electrical system to maintain a consistent equilibrium between the generation and consumption of electric power. In this paper, the PSS is evaluated for a “hybrid power plant” (HPP) which combines thermal, wind, solar photovoltaic (PV), and hydropower generation in Niigata City. A new method for estimating its PV power generation is also introduced based on NHK (the Japan Broadcasting Corporation)’s cloud distribution forecasts (CDFs) and land ratio settings. Our objective is to achieve frequency stability (FS) while reducing CO₂ emissions in the power generation sector. So, the PSS is evaluated according to the results in terms of the FS variable. Six-minute autoregressive wind speed prediction (6ARW) support is used for wind power (WP). One-hour GPV wind farm (1HWF) power is computed from the Grid Point Value (GPV) wind speed prediction data. The PV power is predicted using autoregressive modelling and the CDFs. In accordance with the daily power curve and the prediction time, we can support thermal power generation planning. Actual data on wind and solar are measured every 10 min and 1 min, respectively, and the hydropower is controlled. The simulation results for the electricity frequency fluctuations are within ±0.2 Hz of the requirements of Tohoku Electric Power Network Co,. Inc. for testing and evaluation days. Therefore, the proposed system supplies electricity optimally and stably while contributing to reductions in CO₂ emissions. Full article

In recent years, due to the spike in natural gas spot prices, gas-fired power corporations’ operating costs have skyrocketed. Traditional power generation corporations have gradually been withdrawing from gas power generation investment, replaced by oil and gas enterprises with upstream resources. The development of gas-fired power plants helps to maintain the stability of the power grid and has a positive effect on the realization of carbon neutrality goals. At present, most of the financial evaluation methods for gas power generation projects tend to focus on the static tariffs of the project itself and lack consideration for the overall contribution to the industry chain and the latest “gas–electricity price linkage” mechanisms in China, leading to oil and gas enterprises reducing investment in gas-fired power plants due to yield constraints. In this paper, a financial evaluation methodology for gas power generation projects based on the industrial chain and the “gas–electricity price linkage” mechanism was proposed. The investment return characteristics of specific gas power generation projects under the “gas–electricity price linkage” mechanism in different provinces were revealed through this methodology. Considering the characteristics and industrial development trends in major provinces in China, investment and operation strategies for gas power generation were proposed. These studies provide oil and gas enterprises with references and suggestions for future investment decisions for new gas power generation projects. Full article

We describe enhancements to an operational forecast system based on the Weather Research and Forecasting (WRF) model for the prediction of high-impact weather events affecting power utilities, particularly conditions conducive to wildfires. The system was developed for Pacific Gas and Electric Corporation (PG&E) to forecast conditions in Northern and Central California for critical decision-making such as proactively de-energizing selected circuits within the power grid. WRF forecasts are routinely produced on a 2 km grid, and the results are used as input to wildfire fuel moisture, fire probability, wildfire spread, and outage probability models. This forecast system produces skillful real-time forecasts while achieving an optimal blend of model resolution and ensemble size appropriate for today’s computational resources afforded to utilities. Numerous experiments were performed with different model settings, grid spacing, and ensemble configuration to develop an operational forecast system optimized for skill and cost. Dry biases were reduced by leveraging a new irrigation scheme, while wind skill was improved through a novel approach involving the selection of Global Ensemble Forecast System (GEFS) members used to drive WRF. We hope that findings in this study can help other utilities (especially those with similar weather impacts) improve their own forecast system. Full article

The advent of smart grids has facilitated data-driven methods for detecting electricity theft, with a preponderance of research efforts focused on user electricity consumption data. The multi-dimensional power state data captured by Advanced Metering Infrastructure (AMI) encompasses rich information, the exploration of which, in relation to electricity usage behaviors, holds immense potential for enhancing the efficiency of theft detection. In light of this, we propose the Catch22-Conv-Transformer method, a multi-dimensional feature extraction-based approach tailored for the detection of anomalous electricity usage patterns. This methodology leverages both the Catch22 feature set and complementary features to extract sequential features, subsequently employing convolutional networks and the Transformer architecture to discern various types of theft behaviors. Our evaluation, utilizing a three-phase power state and daily electricity usage data provided by the State Grid Corporation of China, demonstrates the efficacy of our approach in accurately identifying theft modalities, including evasion, tampering, and data manipulation. Full article

This study investigates the role and impact of artificial intelligence (AI) in the electric power industry through a thematic analysis of corporate communications. As AI technologies proliferate, industries—such as the electric power industry—are undergoing significant transformations. The research problem addressed in this study involves understanding how electric power companies perceive, adopt, and implement AI, as well as the implications of these developments. By employing a qualitative thematic analysis approach, we examined a corpus of corporate communications from innovation leaders, including annual reports and sustainability reports, in the electric power sector. The data spanned 2020 to 2023, capturing a crucial period of AI integration in the industry. Our analysis reveals several key findings. Firstly, there is a clear trend toward increased utilization of AI in various facets of the electric power sector, including grid management, predictive maintenance, and customer service. Companies actively invest in AI technologies to enhance operational efficiency, reduce costs, and improve service quality. Secondly, the corporate discourse has shifted significantly, with companies emphasizing AI’s role in sustainability efforts. Moreover, our analysis identified challenges and concerns associated with AI adoption in the electric power industry. In conclusion, the thematic analysis of corporate communications provides valuable insights into the evolving landscape of AI in the electric power industry. The findings underscore the transformative potential of AI technologies, highlighting opportunities for enhanced efficiency and sustainability. However, they also emphasize addressing challenges to ensure responsible and beneficial AI integration. This study contributes to the growing literature on AI in industries, offering practical implications for electric power companies, policymakers, and stakeholders navigating the AI-driven future of the sector. Full article

Over the past decade, the widespread adoption of global green energy has emerged as a predominant trend. However, renewable energy sources, such as wind and solar power, face significant wastage due to challenges in energy storage. Electric vehicles (EVs) are considered an effective solution to address the energy storage dilemma. “Vehicle-to-grid” (V2G) technology, allowing vehicles to feed electricity into the grid, enhances the efficiency of renewable energy utilization. This paper proposes an electric vehicle game model that comprehensively considers user choices, corporate profits, and green energy utilization. The model, based on difference in prices, electricity rates, and fuel prices, establishes user utility models to determine optimal driving distances and driving decisions. It separately formulates the maximum profit functions for selling conventional electric cars and V2G electric cars, deriving optimal pricing for enterprises and user adoption rates. The research findings indicate that when price difference can offset V2G battery costs, increasing price difference and reducing battery costs effectively enhance electric vehicle adoption rates, increase corporate profits, and improve green energy utilization. Moreover, compared to conventional electric vehicles, V2G electric vehicles demonstrate a comparative advantage, with the implementation of V2G expanding corporate profits and green energy utilization. Validation using Chinese data reveals that when price difference can offset V2G battery costs, drivers are more inclined to choose V2G electric vehicles. Both battery electric vehicles (BEVs) and V2G electric vehicles exhibit adoption rates that can increase by over 35%. This study provides theoretical and model support for the future development of V2G and policy formulation, underscoring the comparative advantages of V2G in enhancing green energy utilization efficiency. Additionally, this study offers valuable insights as a reference for business models in the V2G electric vehicle industry. Full article

To enhance the accuracy of theft detection for electricity consumers, this paper introduces a novel strategy based on the fusion of the dual-time feature and deep learning methods. Initially, considering electricity-consumption features at dual temporal scales, the paper employs temporal convolutional networks (TCN) with a long short-term memory (LSTM) multi-level feature extraction module (LSTM-TCN) and deep convolutional neural network (DCNN) to parallelly extract features at these scales. Subsequently, the extracted features are coupled and input into a fully connected (FC) layer for classification, enabling the precise detection of theft users. To validate the method’s effectiveness, real electricity-consumption data from the State Grid Corporation of China (SGCC) is used for testing. The experimental results demonstrate that the proposed method achieves a remarkable detection accuracy of up to 94.7% during testing, showcasing excellent performance across various evaluation metrics. Specifically, it attained values of 0.932, 0.964, 0.948, and 0.986 for precision, recall, F1 score, and AUC, respectively. Additionally, the paper conducts a comparative analysis with mainstream theft identification approaches. In the comparison of training processes, the proposed method exhibits significant advantages in terms of identification accuracy and fitting degree. Moreover, with adjustments to the training set proportions, the proposed method shows minimal impact, indicating robustness. Full article

The use of electric motorcycles (EMCs) is rapidly increasing because EMCs are comparable in price to internal combustion engine motorcycles (ICE motorcycles), can be charged at home, and do not cause pollution. However, using EMCs in residential electrical systems is still a new issue in Thailand, as the Thai power grid was not originally designed to support electric vehicle charging. Therefore, the effect that may occur on the electrical system of a house should be studied. In this study, the power quality when charging an EMC in a residential electrical system is investigated by considering the circuits of various electrical devices according to their actual consumption behavior. Three electric motorcycles with battery capacities of 20, 30, and 40 Ah were used to investigate the effects of charging these motorcycles through the electrical system of a house. The experiment was conducted in a laboratory that replicated the electrical system of a house, and the conditions and patterns of power consumption were identical in all three cases. The test results were considered in terms of power quality, voltage harmonics, and current power system harmonics to analyze the effects on the electrical system in each circuit and to compare the charging differences of each motorcycle model. Next, it was determined that using an EMC is more cost-effective than using an ICE motorcycle. ICE motorcycles will eventually be completely replaced by EMCs, and our research will enable informed decision-making for electric motorcycle riders, researchers, and automotive corporations. Full article

The Gantour Phosphatic Basin (GPB) is formed by a sedimentary series of Maastrichtian to the Eocene age, which consists of alternating phosphate layers and sterile levels. This series outcrops in the northern part of the basin, where it is exploited in open-pit mines. The exploration methodology employed by the Office Chérifien des Phosphates (OCP) group to investigate the GPB is based on direct recognition with boreholes drilled on a 500 × 500 m grid. This research is concerned with the compilation and analysis of data collected during several drilling campaigns conducted on the central segment of the GPB, namely the Louta deposit. This research also includes acquiring, processing, and interpreting new geophysical and hydrogeological data. Its main objective is to provide a better understanding of the deep structure of the phosphatic series. Therefore, the present study was carried out according to a multidisciplinary approach that comprises three parts. (i) The first one involves geological modeling of the exploration borehole data using Datamine Studio RM software (version 1.4), developed by Datamine Corporate Ltd., (Bristol, United Kingdom). It results in establishing a series of geological cross-sections that display a detailed view of the deep structure of the phosphatic series and its lateral variations. (ii) The second part is related to the hydrogeological study, whose purpose was to elaborate on an accurate and updated piezometric map of the study area. The new map helps understand the groundwater flow in the Louta deposit. Furthermore, the superimposition of the piezometric level with the geological sections throws light on the flooded volume of phosphate in this deposit. (iii) The third part of the study focuses on implementing the Electrical Resistivity Tomography (ERT) method. The interpretation of the recorded geoelectrical data not only highlights the main features controlling the mode and the proportion of the phosphate series deepening under the Plio-Quaternary cover but also confirms the evolution of the overall structure of the studied area. The obtained 2D ERT models generally corroborate the cross-sections produced by geological modeling. They also correlate with the information provided by the hydrogeological study. Such information will help guide future hydrogeological and mining extraction planning in the studied area. Full article

Despite the widespread use of artificial intelligence-based methods in detecting electricity theft by smart grid customers, current methods suffer from two main flaws: a limited amount of data on electricity theft customers compared to that on normal customers and an imbalanced dataset that can significantly affect the accuracy of the detection method. Additionally, most existing methods for detecting electricity theft rely solely on one-dimensional electricity consumption data, which fails to capture the periodicity of consumption and overlooks the temporal correlation of customers’ electricity consumption based on their weekly, monthly, or other time scales. To address the mentioned issues, this paper proposes a novel approach that first employed a time series generative adversarial network to balance the dataset by generating synthetic data for electricity theft customers. Then, a hybrid multi-time-scale neural network-based model was utilized to extract customers’ features and a CatBoost classifier was applied to achieve classification. Experiments were conducted on a real-world smart meter dataset obtained from the State Grid Corporation of China. The results demonstrated that the proposed method could detect electricity theft by customers with a precision rate of 96.64%, a recall rate of 96.87%, and a significantly reduced false detection rate of 3.77%. Full article

The increasing penetration of renewable and distributed resources signals a global boom in energy transition, but traditional grid utilities have yet to share in much of the triumph at the current stage. Higher grid management costs, lower electricity prices, fewer customers, and other challenges have emerged along the path toward renewable energy, but many more opportunities await to be seized. Most importantly, there are insufficient studies on how grid utilities can thrive within the hydrogen economy. Through a case study on the State Grid Corporation of China, we identify the strengths, weaknesses, opportunities, and threats (SWOT) of grid utilities within the hydrogen economy. Based on these factors, we recommend that grids integrate hydrogen into the energy-as-a-service model and deliver it to industrial customers who are under decarbonization pressure. We also recommend that grid utilities fund a joint venture with pipeline companies to optimize electricity and hydrogen transmissions simultaneously. Full article

Ensuring access to affordable, reliable, sustainable, and modern energy, as declared in the United Nations’ Agenda 2030, requires both the inclusion of new renewable energy sources, and the renovation of existing hydropower infrastructure, since this resource is considered a key strategy to support flexibility in electric grids with high penetrations of variable generation. This paper addresses the design of a self-supporting lining for the renovation of a headrace tunnel, that has been affected by a buckling event, in order to extend the operating life of the Chivor Hydropower Project, located in Colombia. Studies performed by AES Corporation about the buckling events that affected the headrace tunnel and the condition assessment are first described. Then, the design alternatives to renovate this important part of the hydropower plant’s infrastructure are presented in a general way. The detailed design and construction planning for the selected alternative are then illustrated by showing some calculations used in hydropower design. Such a renovation project is one of the first of its class in Colombia and goes from studies of the buckling events to the design of a modern lining that will be constructed while keeping the 1000-MW (6% of Colombia’s demand) hydropower plant in operation conditions, in order to extend its life for 50 more years, which represents an example for managers and practitioners of large-scale hydraulic engineering projects. Full article

In the smart grid (SG), user consumption data are increasing very rapidly. Some users consume electricity legally, while others steal it. Electricity theft causes significant damage to power grids, affects power supply efficiency, and reduces utility revenues. This study helps utilities reduce the problems of electricity theft, inefficient electricity monitoring, and abnormal electricity consumption in smart grids. To this end, an electricity theft dataset from the state grid corporation of China (SGCC) is employed and this study develops a novel model, a mixture of convolutional neural network and gated recurrent unit (CNN-GRU), for automatic power theft detection. Moreover, the hyperparameters of the proposed model are tuned using a meta-heuristic method, the cuckoo search (CS) algorithm. The class imbalance problem is solved using the synthetic minority oversampling technique (SMOTE). The clean data are trained and then tested with the proposed classification. Extensive simulations are performed based on real energy consumption data. The simulated results show that the proposed theft detection model (CNN-GRU-CS) solved the theft classification problem better than other approaches in terms of effectiveness and accuracy by 10% on average. The calculated accuracy of the proposed method is 92% and the precision is 94%. Full article

Electricity theft harms smart grids and results in huge revenue losses for electric companies. Deep learning (DL), machine learning (ML), and statistical methods have been used in recent research studies to detect anomalies and illegal patterns in electricity consumption (EC) data collected by smart meters. In this paper, we propose a hybrid DL model for detecting theft activity in EC data. The model combines both a gated recurrent unit (GRU) and a convolutional neural network (CNN). The model distinguishes between legitimate and malicious EC patterns. GRU layers are used to extract temporal patterns, while the CNN is used to retrieve optimal abstract or latent patterns from EC data. Moreover, imbalance of data classes negatively affects the consistency of ML and DL. In this paper, an adaptive synthetic (ADASYN) method and TomekLinks are used to deal with the imbalance of data classes. In addition, the performance of the hybrid model is evaluated using a real-time EC dataset from the State Grid Corporation of China (SGCC). The proposed algorithm is computationally expensive, but on the other hand, it provides higher accuracy than the other algorithms used for comparison. With more and more computational resources available nowadays, researchers are focusing on algorithms that provide better efficiency in the face of widespread data. Various performance metrics such as F1-score, precision, recall, accuracy, and false positive rate are used to investigate the effectiveness of the hybrid DL model. The proposed model outperforms its counterparts with 0.985 Precision–Recall Area Under Curve (PR-AUC) and 0.987 Receiver Operating Characteristic Area Under Curve (ROC-AUC) for the data of EC. Full article