Transformer Overloading Research Articles

Short-Term Heavy Overload Forecasting of Public Transformers Based on Combined LSTM-XGBoost Model

In order to effectively carry out the heavy overload monitoring and maintenance of public transformers in the distribution network, ensure the reliability of the distribution network power supply, and improve customer satisfaction with electricity consumption, this paper presents a short-term heavy overload forecasting method for public transformers based on the LSTM-XGBOOST combined model. The model extracts heavy overload feature variables from four dimensions, including basic parameter information, weather, time, and recent load, and constructs a short-term second highest load prediction model based on the LSTM algorithm to obtain the predicted value of the second highest load rate. After aggregating the heavy overload feature variables and the predicted second highest load rate, the XGboost algorithm is employed to construct a short-term heavy overload prediction model for public transformers to judge whether the public transformers display heavy overload. The test results show that this method has high accuracy in short-term heavy overload forecasting, and can effectively assist in the key monitoring and control of heavy overload in public transformers.

Georeferenced rural distribution network model considering scalable growth of users in rural areas

This paper presents a georeferenced optimal planning model of a rural distribution network for deploying an underground network considering a real scenario for increasing electrification in this area. A model is presented based on a heuristic process that minimizes the resources required to route the grid for a defined time horizon. Then, the model is scalable according to the population density, the established and georeferenced zone, and the specified period; that is, it adjusts to any of these variables. Additionally, the modeling is presented in CYME software, considering a series of technical criteria to provide the actual scenario with a verifiable planning model of an underground network. Therefore, the voltage drop and level of overload in transformers and feeders are considered to deliver an optimum power quality to the end-user. These results will be tabulated and presented as an alternative to different planning and design models that an electric distribution company may have to minimize the resources used in an underground deployment.

Impact of Heat Pumps, Rooftop PV, and Hydrogen Blending on Gas-Electricity Distribution Networks in Northeast US

The building sector is responsible for about 7% of overall greenhouse gas emissions in the US. Cutting the emissions by electrifying heating and cooling supply through heat pumps (HPs) leads to an increase in electricity demand and potential overloading of lines and transformers in electricity distribution systems. Although many studies investigate the maximum potential for HPs in existing distribution systems in Europe, they neglect a potential relieving effect of combining HPs with rooftop photovoltaic (PV) systems as well as the consequence of coupling the electricity and gas system at distribution level. Hence, we investigate the effect of HPs and rooftop PV systems in a representative distribution system in Northeast US and the potential of coupling electricity and gas distribution systems. We show that generally no overloading in average US electric distribution systems occurs even under high realistic HP and PV adoption rates. Moreover, our results show that combining HPs and rooftop PV reduces the impact on the distribution system throughout the year with the greatest reduction in spring and fall. In contrast, the potential for injecting hydrogen on distribution level is technically very limited and not economic. Thus, electrolyzers at distribution level are not able to reduce congestion in the electricity system.

A Decentralized Dynamic Pricing Model for Demand Management of Electric Vehicles

Transportation electrification is considered a green alternative to internal combustion vehicles. However, higher penetration of electric vehicles (EVs) can cause several technical challenges to the power systems, including local equipment overloading. This is especially challenging for distribution systems due to the direct connection of EVs with them. To mitigate equipment overloading, by managing EV loads locally, a dynamic pricing model is proposed in this study. First, a satisfaction function is devised for EVs considering the sensitivity of different EV owners to the battery state of charge and urgency of recharge. Then, a welfare function is developed for the EV fleet operator (EFO) considering the net load of EVs and the electricity price of the upstream grid. To this end, a welfare maximization problem is formulated considering the welfare of EVs and EFO. The problem is then decomposed into the EFO sub-problem and EV sub-problem to preserve privacy. Finally, a distributed mechanism is developed to solve the sub-problems iteratively without revealing private information. The performance of the proposed method is analyzed for a residential apartment complex in terms of load management and convergence for different day types (working days and holidays). Simulation results have shown the efficacy of the proposed method in mitigating the overloading of transformers during peak load hours.

Power transformer overload risk evaluation based on temperature rise simulation and fuzzy analytic hierarchy process

The evaluation of transformer overload risk and operational safety is a systematic process. Selecting an appropriate method to accurately assess its overload operation risk is an urgent need for flexible control and safe and reliable operation of power systems. According to the thermal characteristics and state of the transformer, a method for evaluating the overload risk of transformers with improved accuracy based on temperature rise simulation calculation combined with the Fuzzy Analytic Hierarchy Process (FAHP) is proposed. The weights are calculated by fuzzy set theory and FAHP, and the overload risk of the transformer is finally determined. The method comprehensively considers the possible information conflict between the working condition data and the thermal characteristics of the structure itself, and the calculation results are accurate and the process is clear. The calculation results after substituting the actual data show that the method can accurately evaluate the overload risk and operational risk of the transformer. This method is beneficial to identify the relative importance of transformer overload risk factors, calculate transformer overload risk coefficients, and formulate transformer risk control methods.

A Novel Method of Transformer Overloading Detection by Magnetic Flux Leakage Measurements

A Novel Method of Transformer Overloading Detection by Magnetic Flux Leakage Measurements

Огляд європейських тенденцій розвитку електротранспорту та його взаємодії з енергетичним сектором

An overview of the trends in the development of electric transport in European countries is presented, including the state and tax policy of supporting electric transport owners, the dynamics of the growth of the European fleet of electric vehicles, the development of charging infrastructure and the main principles of the interaction between electric vehicles and power system. The experience of countries that have successfully developed electric transport was reviewed, and it was found that the demand for electric cars mostly depends on the price of electric cars for the end user, infrastructure development and government incentive programs. The review also showed that, in practice, the wide spread of electric vehicles should occur simultaneously with the development of the corresponding infrastructure, as well as the development of systems for their interaction with the energy system and compliance with global environmental standards. The growing number of electric vehicles certainly requires special attention from network and power system operators, because the appearance of charging stations of various manufactures and capacities, installed in residential areas, private and apartment buildings, can lead to the number of problems, the emergence of which is associated with electromagnetic compatibility, overloading of electric cables and distribution transformers, safety of operation, that as a result can negatively affect the reliability and quality of power supply. There is also the potential risk that with a certain number of EVs, there will not be enough existing generation capacity and capacity of the electricity grid to charge EV batteries. The analysis of the principles of interaction between electric vehicles and power system in Europe showed that they are based on the concept of Smart Grid - smart or intelligent power supply networks, by means of which the electric vehicle charging system can be managed by the energy supplying company in order to solve a number of energy system tasks: increasing the efficiency of network infrastructure using, peak load shift and the development of smart grid infrastructure. Keywords: electric vehicle, electric charging station, charging infrastructure, power system, environmental requirements, the Smart Grid concept

Double-layer optimized configuration of distributed energy storage and transformer capacity in distribution network

In order to solve the problem of low utilization of distribution network equipment and distributed generation (DG) caused by expansion and transformation of traditional transformer capacity, considering the relatively high cost of energy storage at this stage, a coordinated capacity configuration planning method for transformer expansion and distributed energy storage (DES) is proposed in this paper. First, the energy storage capacity requirements is analyzed on the basis of the transformer overload requirements, and analyzing the correspondence between different capacities of energy storage and transformer expansion capacities. Besides, taking into account the impact of different action mechanisms of energy storage on the node load within a day, a location optimization method for DES based on the standard deviation of network loss sensitivity is introduced. Then, considering the net cost of coordinated planning of energy storage and transformer are minimum and the benefit of energy storage operation is maximum, a two-layer optimization model of distributed energy storage and transformer capacity is established. Finally, the solution method of the two-layer optimization model is proposed, and the feasibility of the proposed model is verified by IEEE33 bus distribution system. The results show that the coordinated planning method proposed in this paper can greatly reduce the investment cost, and the net cost of the coordinated planning scheme is reduced by 17.558 million yuan compared with the scheme of separate configuration for energy storage, which effectively improves the economics of energy storage configuration.

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.

A cooperative EV charging scheduling strategy based on double deep Q-network and Prioritized experience replay

The rapid growth of the number of electric vehicles (EVs) has significantly increased the demand for electricity of residents, which may lead to transformer overload of distribution networks if there is no reasonable power consumption planning. In view of the coordinated charging of EVs under the photovoltaic (PV) energy connected to the power grid, a collaborative charging control strategy is proposed based on a double deep q-network with Prioritized experience replay (DDQN-PER). Specifically, Long–Short-Term Memory (LSTM) neural network is utilized to capture the uncertainties caused by power requirement, PV power, and real-time electricity price. Then, a modified DDQN approach and the corresponding reward function are applied to solve the cooperative charging problem. In addition, the introduction of a PER mechanism based on TD bias sampling in reinforcement learning alleviates the problem of reward sparsity in learning scenarios and improves the training stability and efficiency. The proposed approach can achieve collaborative scheduling of EVs, which helps reduce the charging cost, promote the consumption of PV energy and avoid transformer overload. Simulation case studies based on real data demonstrate that the proposed algorithm can reduce the charging cost by 30% and increase the PV power utilization by 10% compared to the DDQN algorithm.

Mitigating Adverse Impacts of Increased Electric Vehicle Charging on Distribution Transformers

As the world is transitioning to electric vehicles (EVs), the existing power grids are facing several challenges. In particular, the additional charging power demand may repeatedly overload the traditionally-sized distribution transformers and adversely impact their operational life. To address this challenge, this paper proposes an EV-based reactive power compensation strategy for transformer overloading mitigation. Specifically, a low-bandwidth centralized recursive controller is proposed to determine a set point for the EV’s onboard charger’s reactive power. Importantly, the proposed strategy is practically implementable in existing distribution grids as it does not rely on smart grid infrastructure and is stable under potential communication delays and partial failures. This paper discusses the controller’s structure, design, and stability in detail. The proposed solution is tested with a realistic secondary distribution system considering four different EV charging scenarios with both Level 1 and Level 2 residential EV charging. Specifically, IEEE Standard C57.91-2011 is used to quantify the impact of EV charging on the transformer’s life. It is shown that with the proposed method, transformer overloading is significantly reduced, and the transformer’s life improves by an average of 47% over a year in all four scenarios.

Evaluation of Electric Power Losses on 33/11 kV Distribution Feeder Networks in Uyo Urban, Nigeria Using Loss Factor Approach

No matter how carefully a power system is designed, grid inefficiencies or load losses would happen, causing power unbalance between distribution and major load centers. It is therefore fundamental to clearly determine and identify electric power system losses for network improvement because of their financial-economic value to distribution companies and consumers. In this work, relevant data including monthly loading, feeder route length, and cross-sectional area of four 11 kV feeders namely, Aka, Ibrahim Babangida, Udo Udoma, and Idongesit Nkanga secretariat-dedicated feeders were obtained from the Port Harcourt Electricity Distribution (PHED) company technical office, Uyo, Nigeria for four years (January 2018 to December 2021) for the 33/11 kV Akwa Ibom State Secretariat Injection Substation and distribution feeder circuits. Average load, maximum load, load factor, loss factor, power losses in the feeders and total power loss in the system were computed and the results which were shown graphically revealed grid inefficiencies and power imbalance or losses which increased slightly on a yearly basis. The losses are due to heat dissipation, lack of maintenance of the power system components, transformer overloading, copper losses, core losses, lengthy feeder routes, and location/aging of the transformers. Suggestions were made in order to reduce further losses in the network.

Design and Sizing of Electric Bus Flash Charger Based on a Flywheel Energy Storage System: A Case Study

At present, the trend of all sectors and industries, especially the oil and gas industry, is towards reducing carbon dioxide emissions. Along with the modernization of technological processes, special attention has been paid to the reduction in greenhouse gas emissions from vehicles that run on gasoline and diesel. An effective solution in this field is the transition from vehicles with combustion engines to electric vehicles, similar to the use of the electric bus in public transportation. How to charge these electric buses is a challenge for researchers. By proposing a flash charging method for electric buses, the feasibility of using these buses is obtained. Due to the pulsating nature of the power demand in this charging method, the prevention of negative effects on the network by this type of load should be considered. These negative effects can include power quality problems, voltage drop, frequency instability, and overload of transmission lines and transformers. This paper presents a flywheel energy storage system (FESS)-based flash charging station for electric buses. The specifications of the components of this charging station are designed and sized for a case study for line 1 of Tehran Bus Rapid Transit (BRT). A charging strategy based on the proposed charging cycle is presented to minimize the high-power short-duration demand from the grid. The energy consumption of the electric bus based on the electricity consumption model is calculated. Based on theoretical calculations, for the case study, there is a need for installing 12 flash charging stations based on FESS in line 1 Tehran BRT. In this line, an electric bus with a battery capacity of 80 kWh is proposed. The power and energy capacity of these charging stations are sized to 600 kW and 3.334 kWh, respectively. Additionally, an economic comparison regarding the proposed charging station is conducted. The theoretical results of the design and sizing of the proposed charging station are validated based on simulation and experimental results for a small-scale laboratory setup.

A Review of Methods for Assessing DER Hosting Capacity of Power Distribution Systems

This paper presents a comprehensive bibliographic review of the methods for assessing the capacity of electric power distribution systems to host distributed energy resources (DER). Through a structured search at the IEEE Xplore and Scopus databases, this review identifies that the main performance indexes used for hosting capacity assessment are voltage issues, line and transformer overloading, protection issues, and harmonics. This review also identifies that, in the literature, the main approaches for assessing the hosting capacity are: simplified methods, deterministic load flow, stochastic load flow-based methods, and optimization-based methods. Each one of them is critically discussed and summarized. The paper also provides recommendations for new hosting capacity studies.

A Deep RL-Based Algorithm for Coordinated Charging of Electric Vehicles

The development of electric vehicle (EV) industry is facing a series of issues, among which the efficient charging of multiple EVs needs solving desperately. This paper investigates the coordinated charging of multiple EVs with the aim of reducing the charging cost, ensuring a high battery state of charge (SoC), and avoiding the transformer overload. To this end, we first formulate the EV coordinated charging problem with the above multiple objectives as a Markov Decision Process (MDP) and then propose a multi-agent deep reinforcement learning (DRL)-based algorithm. In the proposed algorithm, a novel interaction model, i.e., communication neural network (CommNet) model, is adopted to realize the distributed computation of global information (namely the electricity price, the transformer load, and the total charging cost of multiple EVs). Moreover, different from the most existing works which make specific constraints on the size, the location, or the topology of the distribution network, what we need in the proposed method is only the transformer load. Besides, due to the use of long and short-term memory (LSTM) for price prediction, the proposed algorithm can flexibly deal with various uncertain price mechanisms. Finally, simulations are presented to verify the effectiveness and practicability of the proposed algorithm in a residential charging area.

Electric Vehicle Charging Model in the Urban Residential Sector

Electric vehicles (EVs) have become increasingly popular because they are highly efficient and sustainable. However, EVs have intensive electric loads. Their penetrations into the power system pose significant challenges to the operation and control of the power distribution system, such as a voltage drop or transformer overloading. Therefore, grid operators need to prepare for high-level EV penetration into the power system. This study proposes data-driven, parameterized, individual, and aggregated EV charging models to predict EV charging loads in the urban residential sector. Actual EV charging profiles in Saskatchewan, Canada, were analyzed to understand the characteristics of EV charging. A location-based algorithm was developed to identify residential EV charging from raw data. The residential EV charging data were then used to tune the EV charging model parameters, including battery capacity, charging power level, start charging time, daily EV charging energy, and the initial state of charge (SOC). These parameters were modeled by random variables using statistic methods, such as the Burr distribution, the uniform distribution, and the inverse transformation methods. The Monte Carlo method was used for EV charging aggregation. The simulation results show that the proposed models are valid, accurate, and robust. The EV charging models can predict the EV charging loads in various future scenarios, such as different EV numbers, initial SOC, charging levels, and EV types (e.g., electric trucks). The EV charging models can be embedded into load flow studies to evaluate the impact of EV penetration on the power distribution systems, e.g., sustained under voltage, line loss, and transformer overloading. Although the proposed EV charging models are based on Saskatchewan’s situation, the model parameters can be tuned using other actual data so that the proposed model can be widely applied in different cities or countries.

Decentralized cooperative approach for electric vehicle charging

The proliferation in the deployment of electric vehicles has brought up several challenges in the planning and operation of power systems. The health of power system components and the satisfaction of electric vehicle users are becoming equally important with the increasing presence of electric vehicles on the power grid. The charging strategy is crucial as it plays a significant role in shaping users’ behavior to deal with the adverse effects of electric vehicles on grid components. There is a lack of a proper unified framework for the charging management, which considers both the system perspectives such as transformer overloading, shortage of power, and users perspectives such as satisfaction, charging urgency, and privacy. This paper presents a fully decentralized cooperative approach/strategy for charging electric vehicles. The solution is achieved by developing a decentralized iterative algorithm based on a consensus concept to protect the privacy of EV owners. The charging priority factor is incorporated to address the charging urgency of electric vehicles. Multiple electric vehicles can be charged simultaneously without a central decision-maker or coordinator using the proposed strategy. As a result, electric vehicles fulfill their charging needs by the deadline to maximize user satisfaction while respecting their constraints and limitations imposed by the available charging power and charging station capacity. The convergence of the proposed approach is demonstrated by observing the charging of multiple electric vehicles. The results are benchmarked against the centralized approach to verify the effectiveness of the proposed approach.

The role of residential distributed energy resources in Pakistan's energy transition

In light of climate change concerns and falling costs, many low-income countries, such as Pakistan, have adopted a number of policies to incentivise distributed energy resources and generation. These policies have meant that the installed base of grid-connected distributed solar generation in Pakistan has grown rapidly, from only 1 MW in 2015–2016 to 232 MW in 2020–2021. However, it still constitutes a paltry 0.5% of the overall electricity mix. In this paper, using high-resolution demand and generation profiles from Lahore (the second most populous city in Pakistan and the one with the most distributed generation), we show that distributed generation offers low payback periods for urban households, even without net metering. This calculus is aided by likely future tariff hikes. Additionally, distributed generation helps support the grid, even without storage or demand response, due to a close alignment between supply and demand. Based on this analysis, we recommend the regulator to provide longer term visibility on the continuation of incentives such as net metering, and how this will eventually be shifted towards solar mandates. Likewise, it is prudent for distribution companies to provide targeted incentives for uptake of distributed generation to alleviate issues such as non-technical losses and transformer overloading.

Power supply enhancement in Onitsha distribution network using distribution generations

The research explored ways in which power supplied in Onitsha distribution line network would be enhanced using distributed generation. The problem of insufficient power supply in densely populated metropolis which is characterised by frequent load shedding was solved using optimal placement and sizing of multiple distributed generation. A gas plant was optimally placed at 33 kV injection substations at Awada and General Cotton Mill (GCM). The generators were optimally placed using voltage sensitivity index factor, while their optimum capacities were sized using step method. Simulations were done by power system analysis toolbox (PSAT) 2.1.11 simulation tool in MATLAB 2018a environment and the result of the findings revealed that 32 buses out of 34 buses were violated. The result of the simulation also showed that the optimal operating power output of the generators at Awada and GCM of 12.5 MW and 2.5 MW respectively could only clear 29 out of the 32 bus voltage violations. Overloading of Fegge injection substation transformer was identified as the cause and its upgrade from 7.5 MVA to 15.0 MVA cleared all the voltage violations. The system active loss and reactive loss were 1.82 MW and 35.03 MVar and the research was able to reduce them by 23.30% and 23.80% respectively. The statutory bus voltage limits of ±5% and the requirement that distributed generators installed must be less than the maximum demanded had been achieved in the research. The power supply in Onitsha distribution network was enhanced using distribution generations and the usage of optimal placement and sizing of multiple distributed generation is therefore recommended for problematic distribution networks not only for loss reduction also for better performance.

A Two-Level Integrated Scheduling Strategy for Vehicle-Network Synergy considering New Energy Consumption

With the substantial increase in the number of electric vehicles, the charging of electric vehicles without regulation and scale control will bring about problems, such as overloading of distribution transformers; the proportion of new energy power generation is also increasing year by year, and the access of new energy to the power grid will cause volatility. In order to solve the problems above, this paper proposed a coordinated and orderly scheduling strategy considering new energy consumption, which protects the interests of both users and the integrated power grid. First, a two-level vehicle-network interaction model considering both supply and demand sides was established. The upper-level model optimized the indicators on the distribution grid side, and a term of charge-discharge margin as well as grid-side load variance model was proposed. The lower-level optimization model was set based on the users’ condition. The average discharge rate index was defined to evaluate the battery loss satisfaction in the scheduling strategy, which fully considered users’ charging and discharging cost, and finally achieved a win-win situation between the power grid and the user. Secondly, the fast nondominated sorting genetic algorithm (NSGA-II) was used to figure out the effect of the strategy proposed in this paper, and a community is taken as an example for simulation. The results confirmed the economy and rationality of the above strategy, by rationally scheduling the charging and discharging behavior of electric vehicles, consuming new energy, restraining the fluctuation of the remaining new energy power generation, realizing the dynamic balance between the charging and discharging load and the output of new energy in a certain area, and finally effectively suppressing the fluctuation of the power grid load while improving the availability of clean energy.