Transformer Overloading Research Articles

Battery-Conscious, Economic, and Prioritization-Based Electric Vehicle Residential Scheduling

Advances in communication technologies and protocols among vehicles, charging stations, and controllers have enabled the application of scheduling techniques to prioritize EV fleet charging. From the perspective of users, residential EV charging must particularly address cost-effective solutions to use energy more efficiently and preserve the lifetime of the battery—the most expensive element of an EV. Considering this matter, this research addresses a residential EV charging scheduling model including battery degradation aspects when discharging. Due to the non-linear characteristics of charging and battery degradation, we consider a mixed integer non-linearly constrained formulation with the aim of scheduling the charging and discharging of EVs to satisfy the following goals: prioritizing charging, reducing charging costs and battery degradation, and limiting the power demand requested to the distribution transformer. The results shows that, when EVs are discharged before charging up within a specific state-of-charge range, degradation can be reduced by 5.3%. All charging requests are completed before the next-day departure time, with 16.35% cost reduction achieved by scheduling charging during lower tariff prices, in addition to prevention of overloading of the distribution transformer.

SST-Based Grid Reinforcement for Electromobility Integration in Distribution Grids

Electric Vehicles (EVs) are gaining acceptance due to the advantages they offer in the reduction of nitrogen oxide and carbon dioxide emissions. The need for emission reduction and the potential of EVs for these reductions is reflected in the current sustainable mobility policies of the EU as well as the German government. Increasing the penetration of EVs in the grid requires an expansion of EV charging infrastructure, which in turn requires either grid reinforcement or solutions for more efficient use of existing infrastructure to avoid or postpone grid reinforcement. Distribution transformers face increased loading due to EV charging and need to be protected from overloading during peak load periods to ensure continuity of service. Therefore, transformers are one of the components that are upgraded or replaced as a part of grid reinforcement. In this paper, we propose the connection of a Solid-State Transformers (SST) between two buses operating at the same-voltage level as an alternative to replacement or upgrading of conventional transformer as well as to prevent their overloading. We analyse how the proposed topology can be useful to reduce the impact of EV integration on the overloading of distribution transformers and node voltage violations in the distribution grid.

Impact assessment of grid tied rooftop PV systems on LV distribution network

Due to the high demand for electricity in the world today and the need to reduce carbon footprint, the power system is undergoing a profound change and this change is paving way for renewable electricity generation into the traditional grid, making the conventional network become smarter. The convention which has been in existence over several decades has changed; thus, consumers of electricity have now become prosumers; generating and feeding electricity into the low voltage network. Despite the benefits of prosuming, some researchers say their presence create stability, reliability and power quality (over and under-voltages, and high harmonic distortion levels) problems which in effect lead to overloading of transformers and feeders, extreme line losses, and failure in network protection exceeding the limits in the low voltage grid.The objective of this paper is to evaluate the effect of rooftop PV generations on distribution losses (power losses) and network voltage profile (voltage regulation on LV network) in a typical traditional grid setup. The study employs an empirical analysis of the power system, forward/backward sweep based on BIBC and BCBV matrices and python simulation to arrive at its conclusion under two different scenarios, nine different loading conditions and six PV (from 1kWp to 6kWp) injections.The results obtained show that a grid connected with rooftop PV systems have the potential of reducing distribution losses substantially and also do not violate standardized voltage limits. The net power loss recorded on the network with PV injections from 1kWp to 6kWp were in the range of 1.1kW–88.9kW at poor power factors and 0.7kW and 89.1kW at improved power factors, respectively. The variation in the voltage profile ranges between 0.97pu and 1.05pu for both the conventional and prosumer grids.

BATTPOWER application: Large-scale integration of EVs in an active distribution grid – A Norwegian case study

With the considerable increase of Distributed Energy Resources (DER), reliable and cost-effective operation of distribution grids becomes challenging. The efficient operation relies on computationally dependable and tractable optimisation solvers, which may handle: 1) non-linear AC power flow constraints, and 2) time-linking variables and constraints and objectives of DER, over the operational horizon. In this paper, we introduce an application of a high-performance MultiPeriod AC Optimal Power Flow (MPOPF) solver, called “BATTPOWER”, to simulate active distribution grids for a near-future scenario. A large-scale Norwegian distribution grid along with a large population of Electric Vehicles (EV) are here taken as the case-study. We suggest and analyse three operational strategies (in terms of control of charge scheduling fleet of EV) for the Distribution System Operator (DSO): (a) uncoordinated charge scheduling, (b) coordinated charge scheduling with the objective of energy cost-minimisation without operational constraints of the grid, and (c) coordinated charge scheduling with the objective of energy cost-minimisation along with the operational constraints of the grid. The results demonstrate that the uncoordinated charging would lead to: 1) overloading of lines and transformers when the share of EVs is above 20%, and 2) higher operational costs than the proposed control strategies of (b) and (c). In strategy (b) operational line/transformer limits are violated when the populations of EVs are growing above 36%. This implies that current market design must be altered to allow active control of a large proportion of DERs within grid operational limits to achieve cost minimization at system level. To our knowledge, the work presented in this paper is the first ever attempt to do a comprehensive analysis of the impact of EV charging demand on a real Norwegian distribution grid. Moreover, the inference of the analysis says that the Norwegian distribution networks are more prone to congestion problems than the voltage problems for the EV demand which includes a smart charging scheme accounting for grid conditions.

Limits set by component loadability on solar power integration in distribution networks

This paper proposes a number of deterministic and stochastic approaches to quantify the hosting capacity of the distribution network for solar photovoltaics (PV) units when that hosting capacity is limited by the loadability of feeder cables or distribution transformers. Such approaches should be used as input to investment and planning decisions by distribution-system operators. Distribution networks from two areas in Sweden supplying 309 MV/LV distribution transformers with 12,000 customers downstream have been used to illustrate the different approaches. By defining a break-even length, it is shown that both overload and overvoltage may limit the hosting capacity. The results obtained for the 309 networks show that the overload limit is more often exceeded for transformers than for feeder cables for smaller solar PV sizes. For larger solar PV sizes, feeder cables are likely to be overloaded before the transformers. A stochastic hosting capacity is calculated for 170 of the 309 distribution transformers and compared with a deterministic definition of the hosting capacity. It is shown that the deterministic definition corresponds to a 50% risk of overloading. Both deterministic and stochastic definitions show that transformer overloading is very unlikely as long as the probability of customers installing solar PV is less than 20%.

Impact on Voltage Quality and Transformer Aging of Residential Prosumer Ownership of Plug-In Electric Vehicles: Assessment and Solutions

The effects of climate change have resulted in the increased deployment of plug-in electric vehicles (PEVs), solar photovoltaics (PVs), and energy storage system penetration levels in the residential sector. The integration of these resources with accurate stochastic models is expected to affect the assessment of the electrical distribution system (EDS) assets. This study proposes a new framework for evaluating and enhancing voltage quality, distribution transformer (DT) overload, and aging, while considering residential prosumer ownership of PEVs. The proposed work develops a probabilistic power flow in order to investigate the impact of the stochastic nature of PEVs, PVs, and conventional load. In this work, the residential premises are modeled for supply through a detailed secondary distribution system, which is integrated as a part of the EDS. This article enhances the existing research through the inclusion of PEVspatial–temporal (SAT)-charging activities into the assessment models of DT overload and aging, voltage imbalance, and voltage deviation. The proposed framework provides a more realistic life expectancy for DTs compared with a simplified model in the literature. The results indicate that the use of the proposed SAT-based approach has reduced DT lifetime to 6.30 years from 7.92 years for the same PEV-penetration level.

Smart charging of electric vehicles to minimize the cost of chargingand the rate of transformer aging in a residential distribution network

Electric vehicles (EVs) exhibit several benefits over combustion engine vehicles, making them an attractive mode of mobility for the future. However, supplying the electrical energy required to recharge their batteries could adversely affect the power system infrastructure. The most severe impact of EV integration is expected to be on the distribution transformers, which are among the costliest equipment in the distribution network. Sustained overloads on the transformer could lead to accelerated aging and early retirement. As the rate of EV deployment rises, so does the probability of transformer overloads and the subsequent loss of life. There is a need for smart charging schemes in which the distribution system operator can schedule the charging of EVs in an optimal manner that prevents overloads and extends the transformer's life. However, EV owners might hesitate to surrender the charging control of their vehicles for the utility's benefit alone. Charging-cost minimization has been identified as an effective motivation for EV users to participate in charge management schemes. This paper presents a smart charging scheme, which minimizes the cost of charging EVs by optimizing their charging powers with respect to a real-time pricing tariff. As the electricity price changes dynamically with the system demand, cost minimization is equivalent to network decongestion. A decongested network is less susceptible to overloads and equipment damage. Simulation results show that with smart charging, the charging cost incurred by EV owners as well as the rate of aging undergone by the transformer can be significantly reduced.

Modeling and evaluation of SOC-based coordinated EV charging for powermanagement in a distribution system

The importance of using clean energy in electrical energy generation and transportation network planning has recently increased due to carbon footprint rising. In this direction, the use of electric vehicles (EV), known as ultra-low carbon emission vehicles, has become widespread in addition to renewable energy sources (RES) such as wind and photovoltaic (PV) power generations. The trend of EVs to be preferred the primary means of transport has revealed the effects of charging an additional load on the grid. There is a need to create coordinated charging methods by considering the approaches for real-time charging models of EVs. In this paper, SOC-based EV coordinated charging was proposed for power management to prevent adverse effects including transformer overload, instantaneous peak loading and line overload in the existing distribution network. The proposed coordinated EV charging method was tested on the modified Roy Billinton test system (RBTS) Bus 2 network. AC 11 kW uncoordinated charging units have been respectively 123.76% distribution transformer and 115.16% distribution line overloading for 500 EVs on the grid with 13,9% diversity factor. However, these values that are 72.05% of distribution transformer and 67.01% of distribution grid overloading according to permittable level were decreased by the proposed coordinated charging method. Also, the state of charge (SOC) based coordinated method can increase 3.5% rate the diversity factor of charging capacity at the charging station with PV and battery energy system (BES) while ensured grid stability and energy efficiency.

Optimizing the effect of charging electric vehicles on distribution transformer using demand side management

This paper mainly aims to present the demand side management (DSM) of electric vehicles (EVs) by considering distribution transformer capacity at a residential area. The objective functions are formulated to obtain charging schedule for individual owner by i) minimizing the load variance and ii) price indicated charging mechanism. Both the objective functions profit the owner in the following ways: i) fulfilling their needs, ii) minimizing overall charging cost, iii) lessening the peak load, and iv) avoiding the overloading of distribution transformer. The proposed objective functions were worked on a residential area with 8 houses each house with an EV connected to a 20 kVA distribution transformer. The formulations were tested in LINGO platform optimization modeling software for linear, nonlinear, and integer programming. The results obtained were compared which gives good insight of EV load scheduling without actual price prediction.

Optimization of Energy Efficiency for Electric Power Distribution System Losses

Abstract: This study evaluated the existing electric power network of Mile 2 Diobu zone, Port Harcourt distribution network which consists of four (4) 11kV distribution feeders namely; Ojoto, Nsukka, Udi and Silverbird. This work considered Ojoto and Nsukka Street distribution network for improved power quality. The three (3) 33/11kv injection substations are fed from 165 MVA transmission station (PH Town) at Amadi junction by Nzimiro. Collection and analysis of data collected from the injection substations that supply electricity to mile 2 Diobu, Port Harcourt was the first consideration. The distribution network was modeled in Electrical Transient Analyzer Program (ETAP) using Newton-Raphson Load Flow equations. The simulation result of the existing condition network shows that the network has low voltage profile problem on Nsukka network and overloading of distribution transformers on Ojoto networks. The following optimization techniques are applied: up-gradation of distribution transformers, and transformer load tap changer to improve the distribution network for Mile 2 Diobu, Port Harcourt electrical power network. The simulation result of the improved distribution network for Mile 2 Diobu, Port Harcourt power network shows that the voltage profile Nsukka network has improved within the statutory limit which is between 95.0 -105.0% and the loading of the distribution transformers on Ojoto and Nsukka networks are all below 70% required capacity. Keywords: Optimization, Energy Efficiency Distribution

Challenges in Grid Integration of Electric Vehicles in Urban and Rural Areas

The ramp-up of battery electric vehicles (BEVs) could lead to severe grid issues but also enables flexibility. This paper provides a better understanding of the challenges and potentials of integrating BEVs in power grids. Three charging strategies were modelled on four use cases and six low-voltage grids for urban and rural areas. Especially in rural areas, where many cars charge at home overnight, the charging strategy significantly affects grid issues. Purely market-oriented strategies can lead to high load peaks and thus to transformer and line overloading, while even a relatively simple, balanced charging strategy can significantly reduce grid issues.

Residential Charging Scheduling of Electric Vehicles in Brazilian Context

This paper presents a methodology to schedule the charging of Electric Vehicles (EVs) in order to maintain the loading of distribution transformer under the nameplate load while reducing charging costs. Brazilian consumption characteristics, mobility pattern and tariff structure are considered to design an optimization algorithm. While the demand of dwellings are estimated from typical load curves of a distribution utility located in the South of Brazil, the demand of EVs are estimated from mobility survey data. Adopting the current Brazilian tariff structure, coefficients of the objective function are formulated in order to schedule the charging of EVs to satisfy the following goals: limit the power demand requested to the distribution transformer, cope the demand of EVs with lower demand of dwellings and reduce the charging costs. The results shows that, in addition to prevent the overloading of distribution transformer, the charging only occurs in periods of higher tariff when necessary, whereas most of it occurs during late night and early morning, corresponding to cost reduction by purchasing energy during lower tariff prices

Electric Vehicles Charging Management Using Machine Learning Considering Fast Charging and Vehicle-to-Grid Operation

Electric vehicles (EVs) have gained in popularity over the years. The charging of a high number of EVs harms the distribution system. As a result, increased transformer overloads, power losses, and voltage fluctuations may occur. Thus, management of EVs is required to address these challenges. An EV charging management system based on machine learning (ML) is utilized to route EVs to charging stations to minimize the load variance, power losses, voltage fluctuations, and charging cost whilst considering conventional charging, fast charging, and vehicle-to-grid (V2G) technologies. A number of ML algorithms are contrasted in terms of their performances in optimization since ML has the ability to create accurate future decisions based on historical data, which are Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Long Short-Term Memory (LSTM) and Deep Neural Networks (DNN). The results verify the reliability of the use of LSTM for the management of EVs to ensure high accuracy. The LSTM model successfully minimizes power losses and voltage fluctuations and achieves peak shaving by flattening the load curve. Furthermore, the charging cost is minimized. Additionally, the efficiency of the management system proved to be robust against the uncertainty of the load data that is used as an input to the ML system.

Adaptive Congestion Control for Electric Vehicle Charging in the Smart Grid

This article proposes an adaptive control algorithm for plug-in electric vehicle charging without straining the power system. This control algorithm is decentralized and merely relies on congestion signals generated by sensors deployed across the network, e.g., distribution-level phasor measurement units. To dynamically adjust the parameter of this congestion control algorithm, we cast the problem as multi-agent reinforcement learning where each charging point is an independent agent which learns this parameter using an off-policy actor-critic deep reinforcement learning algorithm. Simulation results on a test distribution network with 33 primary distribution nodes, 1760 low voltage end nodes, and 500 electric vehicles corroborate that the proposed algorithm tracks the available capacity of the network in real-time, prevents transformer overloading and voltage limit violation problems for an extended period of time, and outperforms other decentralized feedback control algorithms proposed in the literature. These results also verify that our control method can adapt to changes in the distribution network such as transformer tap changes and feeder reconfiguration.

The influence of harmonic distortion on losses and efficiency of three-phase distribution transformer

The operation of non-linear load leads to distortion on system and equipment. The nonlinear relation between voltage and current implies higher frequency components of current and voltage called harmonic. This may constitute distortion on the fundamental waveform. If this distorted waveform applies to a transformer, it may lead to additional losses and therefore reduces transformer efficiency. The losses occur due to the degree of harmonic distortion, commonly called total harmonic distortion (THD). This paper discusses the operating condition of three-phase transformer serving nonlinear load in the form of power converter. The related impacts of THD on voltage and current, additional power losses and its efficiency are analysed. The calculation is implemented on a 200 kVA three-phase transformer serving a rectifier supplying a balanced inductive load. For analysis purpose the system is simulated using Simulink in MATLAB environment. From the generated results it is identified the THD of voltage and current distortions of 19% and 18%, respectively. These distortion is analysed to confirm the standard of IEEE 519-2014 verifying the distortions exceeding the maximum limit for both voltage and current. The effect of harmonic on transformer loss is also observed. The loss is calculated taking into account all parameters ensuring the calculation is valid. The result indicates the increase of loss due to considering harmonic distortion from 0.62 kW to 1.854 kW causing reduction of efficiency from 99.65% to 98.97%. This loss addition implies a derating of the transformer to avoid transformer overload that may shorten the device’s lifetime.

A novel communication‐free charge controller for electric vehicles using machine learning

Despite many advantages, wide-spread integration of electric vehicles (EVs) in the power systems is challenging. Large-scale uncontrolled EV charging load may lead to under-voltage violations, higher power losses, overloading of transformers and transmission lines. In this research work, a machine learning-based communication-free EV charge control strategy is developed to mitigate the issues caused by the uncontrolled EV charging. Another notable feature of the proposed controller is to ensure the fairness among the EVs available at different locations in the distribution system. Moreover, two indices are proposed, that is system utilisation index (SUI) and system average voltage violation duration index (SAVVDI), to assess the performance of any EV charge controller. In the proposed control structure, a nodal voltage and the voltage-to-load sensitivity are measured at each load node which are fed to the EV charge controller. In fact, an upstream node is generally less sensitive to the changes in load, since it is closer to the feeding point, as compared to a downstream node. The output of the charge controller is the charging rate of an EV. In order to validate the robustness of the proposed controller, light and heavy loading conditions are considered which mimic the daily, monthly, yearly and seasonal load variations. Simulation results prove that the proposed controller effectively improves the voltage profiles and ensures fairness among the EVs connected at various charging points in the distribution system. Moreover, it effectively utilises the power system resources.

Research and Implementation of Wind and Solar Energy Storage and Charging Scheme

In this paper, according to the characteristics of tourist attractions, the old station area and the heavy load station area which is difficult to increase capacity are reconstructed, and the energy saving and loss reduction treatment scheme is proposed. The wind and wind power generation equipment is used and the energy storage equipment is added to realize the power supply stability of the intelligent substation area distributed system. The transformer overload problem in the peak load period of the substation area is solved, and the energy-saving and loss reduction effect of the distribution line is realized.

Assessment of dynamic transformer rating, considering current and temperature limitations

Nowadays, transformers can be operated close to their thermal limits. The state of the art in transformer thermal limits are Dynamic Thermal Rating (DTR). DTR can be based on two temperature limits: either a design temperature or maximal-permissible temperature. However, many papers estimate DTR with the design temperature only. Therefore, the true DTR is still underestimated since temperature limit is ignored as well as current limits (in some papers). Moreover, many papers rely on the conservative assumption of typical historical load profile or net load profile (considering the distributed generation, storage, electric vehicles). However, modern DSO can control the shape of (net) load profile. This can make a DTR estimation, assuming some typical (net) load profile, valuable only for a particular shape of (net) load profile but not for its modifications. This paper suggests a DTR feasible region which is constructed from current and temperature limitations and does not rely on typical load profiles. As a case study, we investigate DTR in cold and warm climates: one case in Russia with a continental climate and another in France with a temperate climate. In these climates, we assess DTR for the most common combinations of current and temperature limitations, used in standards and literature. As a result, DTR can ensure an additional capacity up to 45%. DTR, based on temperature limits, are 100% of time higher than nominal rating. Moreover, the limiting factors of DTR are quantified based on analysis of 34-year ambient temperature data. Finally, comprehensive recommendations for transformer overloading are formulated.

Optimal Pricing of Vehicle-to-Grid Services Using Disaggregate Demand Models

The recent revolution in electric mobility is both crucial and promising in the coordinated effort to reduce global emissions and tackle climate change. However, mass electrification brings up new technical problems that need to be solved. The increasing penetration rates of electric vehicles will add an unprecedented energy load to existing power grids. The stability and the quality of power systems, especially on a local distribution level, will be compromised by multiple vehicles that are simultaneously connected to the grid. In this paper, the authors propose a choice-based pricing algorithm to indirectly control the charging and V2G activities of electric vehicles in non-residential facilities. Two metaheuristic approaches were applied to solve the optimization problem, and a comparative analysis was performed to evaluate their performance. The proposed algorithm would result in a significant revenue increase for the parking operator, and at the same time, it could alleviate the overloading of local distribution transformers and postpone heavy infrastructure investments.

Application of transformer heavy load and overload analysis and management system

In recent years, with the development of China’s economy and society, the social electricity consumption increases rapidly. As a support for the development of the national economy, the scale of power grid has expanded. The load capacity of oil-immersed power transformer is an important safety and performance index. Limited load capacity or overload may cause a large area of power grid blackout. Therefore, it is necessary to analyze and control the transformer heavy load and overload.