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Photovoltaic Maximum Penetration Limits on Medium Voltage Overhead and Underground Cable Distribution Feeders: A Comparative Study
This paper investigates the maximum photovoltaic (PV) penetration limits on both overhead lines and underground cables medium voltage radial distribution system. The maximum PV penetration limit is estimated considering both bus voltage limit (1.05 p.u.) and feeder current ampacity (1 p.u.). All factors affect the max PV penetration limit are investigated in detail. Substation voltage, load percentage, load power factor, and power system frequency (50 Hz or 60 Hz) are analyzed. The maximum PV penetration limit associated with overhead lines is usually higher than the value associated with the underground cables for high substation voltage (substation voltage = 1.05 and 1.04 p.u.). The maximum PV penetration limit decreases dramatically with low load percentage for both feeder types but still the overhead lines accept PV plant higher than the underground cables. Conversely, the maximum PV penetration increases with load power factor decreasing and the overhead lines capability for hosting PV plant remains higher than the capability of the underground cables. This paper proved that the capability of the 60-Hz power system for hosting the PV plant is higher than the capability of 50 Hz power system. MATLAB software has been employed to obtain all results in this paper. The Newton-Raphson iterative method was the used method to solve the power flow of the investigated systems.
Journal Article
Energy management of islanded microgrid for reliability and cost trade-off with PV, energy storage, and diesel generator
Uncertainties in the solar photovoltaic (PV) power generation, random behaviour of consumer load power demand, and unexpected failures are the major factors for the consumer power interruption (CPI) hours, which reduce the reliable power supply rate or reliability index, of an islanded microgrid (IMG). One way to address this challenge is to complement the IMG with solar PV, energy storage system (ESS), and diesel generator (DG). This study first defines the reliability index of an IMG that equipped with solar PV, ESS, and DG. Then the authors propose an energy management strategy (EMS) for an IMG to maintain the reliability index above a given threshold limit while lowering the cost through higher utilisation of PV, lower CPI hours, and lower DG operating hours. They utilise real IMG consumers load power demand and solar radiance data for simulation studies to show how the proposed EMS achieves the desired trade-off in reliability and cost.
Journal Article
Grid Integration of Electric Vehicles in Open Electricity Markets
Presenting the policy drivers, benefits and challenges for grid integration of electric vehicles (EVs) in the open electricity market environment, this book provides a comprehensive overview of existing electricity markets and demonstrates how EVs are integrated into these different markets and power systems. Unlike other texts, this book analyses EV integration in parallel with electricity market design, showing the interaction between EVs and differing electricity markets. Future regulating power market and distribution system operator (DSO) market design is covered, with up-to-date case studies and examples to help readers carry out similar projects across the world. With in-depth analysis, this book describes: * the impact of EV charging and discharging on transmission and distribution networks * market-driven EV congestion management techniques, for example the day-ahead tariff based congestion management scenario within electric distribution networks * optimal EV charging management with the fleet operator concept and smart charging management * EV battery technology, modelling and tests * the use of EVs for balancing power fluctuations from renewable energy sources, looking at power system operation support, including frequency reserve, power regulation and voltage support An accessible technical book for power engineers and grid/distributed systems operators, this also serves as a reference text for researchers in the area of EVs and power systems. It provides distribution companies with the knowledge they need when facing the challenges introduced by large scale EV deployment, and demonstrates how transmission system operators (TSOs) can develop the existing system service market in order to fully utilize the potential of EV flexibility. With thorough coverage of the technologies for EV integration, this volume is informative for research professors and graduate students in power systems; it will also appeal to EV manufacturers, regulators, EV market professionals, energy providers and traders, mobility providers, EV charging station companies, and policy makers.
eBook
Deep-Learning Forecasting Method for Electric Power Load via Attention-Based Encoder-Decoder with Bayesian Optimization
Short-term electrical load forecasting plays an important role in the safety, stability, and sustainability of the power production and scheduling process. An accurate prediction of power load can provide a reliable decision for power system management. To solve the limitation of the existing load forecasting methods in dealing with time-series data, causing the poor stability and non-ideal forecasting accuracy, this paper proposed an attention-based encoder-decoder network with Bayesian optimization to do the accurate short-term power load forecasting. Proposed model is based on an encoder-decoder architecture with a gated recurrent units (GRU) recurrent neural network with high robustness on time-series data modeling. The temporal attention layer focuses on the key features of input data that play a vital role in promoting the prediction accuracy for load forecasting. Finally, the Bayesian optimization method is used to confirm the model’s hyperparameters to achieve optimal predictions. The verification experiments of 24 h load forecasting with real power load data from American Electric Power (AEP) show that the proposed model outperforms other models in terms of prediction accuracy and algorithm stability, providing an effective approach for migrating time-serial power load prediction by deep-learning technology.
Journal Article
Deep learning-driven hybrid model for short-term load forecasting and smart grid information management
Accurate power load forecasting is crucial for the sustainable operation of smart grids. However, the complexity and uncertainty of load, along with the large-scale and high-dimensional energy information, present challenges in handling intricate dynamic features and long-term dependencies. This paper proposes a computational approach to address these challenges in short-term power load forecasting and energy information management, with the goal of accurately predicting future load demand. The study introduces a hybrid method that combines multiple deep learning models, the Gated Recurrent Unit (GRU) is employed to capture long-term dependencies in time series data, while the Temporal Convolutional Network (TCN) efficiently learns patterns and features in load data. Additionally, the attention mechanism is incorporated to automatically focus on the input components most relevant to the load prediction task, further enhancing model performance. According to the experimental evaluation conducted on four public datasets, including GEFCom2014, the proposed algorithm outperforms the baseline models on various metrics such as prediction accuracy, efficiency, and stability. Notably, on the GEFCom2014 dataset, FLOP is reduced by over 48.8%, inference time is shortened by more than 46.7%, and MAPE is improved by 39%. The proposed method significantly enhances the reliability, stability, and cost-effectiveness of smart grids, which facilitates risk assessment optimization and operational planning under the context of information management for smart grid systems.
Journal Article
Modelling Constant Power Loads: Accounting for Power Losses in Power Converters
A constant power load (CPL) refers to an electrical load that maintains constant power consumption regardless of variations in the supply voltage. Many power electronics devices and converters exhibit such characteristics from the point of view of their DC link due to their control mechanisms and design configurations that aim to regulate output power despite changes in input voltage. This CPL characteristic is often modelled as a constant negative resistance. However, this modelling approach is inaccurate and may not represent the worst‐case scenario because it neglects losses of power converters. In this paper, the effect of power losses in converters on the modelling of CPLs is studied, demonstrating that a lossy model of a power converter can present a more critical situation than a lossless one. The results of the mathematical study are verified using multi‐domain offline and real‐time simulations of a single buck converter with control characteristics of a CPL. In addition, to further show the aforementioned effect, a multi‐converter system has been simulated which its results support the mathematical analysis. The constant power load (CPL) characteristic is often modelled as a constant negative resistance. However, this modelling approach is inaccurate and may not represent the worst‐case scenario because it neglects losses of power converters. In this paper, the effect of power losses in converters on the modelling of CPLs is studied.
Journal Article
Power-Load Forecasting Model Based on Informer and Its Application
Worldwide, the demand for power load forecasting is increasing. A multi-step power-load forecasting model is established based on Informer, which takes the historical load data as the input to realize the prediction of the power load in the future. The constructed model abandons the common recurrent neural network to deal with time-series problems, and uses the seq2seq structure with sparse self-attention mechanism as the main body, supplemented by specific input and output modules to deal with the long-range relationship in the time series, and makes effective use of the parallel advantages of the self-attention mechanism, so as to improve the prediction accuracy and prediction efficiency. The model is trained, verified and tested by using the power-load dataset of the Taoyuan substation in Nanchang. Compared with RNN, LSTM and LSTM with the attention mechanism and other common models based on a cyclic neural network, the results show that the prediction accuracy and efficiency of the Informer-based power-load forecasting model in 1440 time steps have certain advantages over cyclic neural network models.
Journal Article