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"distribution grids"
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Metrological Qualification of PD Analysers for Insulation Diagnosis of HVDC and HVAC Grids
On-site partial discharge (PD) measurements have turned out to be a very efficient technique for determining the insulation condition in high-voltage electrical grids (AIS, cable systems, GIS, HVDC converters, etc.); however, there is not any standardised procedure for determining the performances of PD measuring systems. In on-line and on-site PD measurements, high-frequency current transformers (HFCTs) are commonly used as sensors as they allow for monitoring over long distances in high-voltage installations. To ensure the required performances, a metrological qualification of the PD analysers by applying an evaluation procedure is necessary. A novel evaluation procedure was established to specify the quantities to be measured (electrical charge and PD repetition rate) and to describe the evaluation tests considering the measured influence parameters: noise, charge amplitude, pulse width and time interval between consecutive pulses. This procedure was applied to different types of PD analysers used for off-line measurements, sporadic on-line measurements and continuous PD monitoring. The procedure was validated in a round-robin test involving two metrological institutes (RISE from Sweden and FFII from Spain) and three universities (TUDelft from the Netherlands, TAU from Finland and UPM from Spain). With this round-robin test, the effectiveness of the proposed qualification procedure for discriminating between efficient and inappropriate PD analysers was demonstrated. Furthermore, it was shown that the PD charge quantity can be properly determined for on-line measurements and continuous monitoring by integrating the pulse signals acquired with HFCT sensors. In this case, these sensors must have a flat frequency spectrum in the range between several tens of kHz and at least two tens of MHz, where the frequency pulse content is more significant. The proposed qualification procedure can be useful for improving the future versions of the technical specification TS IEC 62478 and the standard IEC 60270.
Journal Article
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.
Journal Article
Unbalanced multi‐phase distribution grid topology estimation and bus phase identification
There is an increasing need for monitoring and controlling uncertainties brought by distributed energy resources in distribution grids. For such goal, accurate multi‐phase topology is the basis for correlating measurements in unbalanced distribution networks. Unfortunately, such topology knowledge is often unavailable due to limited investment. Also, the bus phase labeling information is inaccurate due to human errors or outdated records. For this challenge, this paper utilizes smart meter data for an information‐theoretic approach to learn the topology of distribution grids. Specifically, multi‐phase unbalanced systems are converted into symmetrical components, namely positive, negative, and zero sequences. Then, this paper proves that the Chow‐Liu algorithm finds the topology by utilizing power flow equations and the conditional independence relationships implied by the radial multi‐phase structure of distribution grids with the presence of incorrect bus phase labels. At last, by utilizing Carson's equation, this paper proves that the bus phase connection can be correctly identified using voltage measurements. For validation, IEEE systems are simulated using three real data sets. The simulation results demonstrate that the algorithm is highly accurate for finding multi‐phase topology even with strong load unbalancing condition and DERs. This ensures close monitoring and controlling DERs in distribution grids.
Journal Article
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.
Journal Article
Distributed architecture for self‐organising smart distribution systems
Automation of emerging smart distribution grids is required to operate the grid efficiently and swiftly. This study draws a vision on grid automation with agent‐based cyber‐physical system integration to provide a truly distributed architecture. Furthermore, this study introduces a notion of self‐organising smart distribution grid that promotes the grid capability to heal and organise itself in the best‐suited topology without the intervention of a central operator. The proposed architecture comprises the system of bus agents (BAs) that emulate the given grid. This emulation is used by BAs to comprehend the grid conditions, switch location and compute their representative bus voltage and partial loss and to estimate the best organisation of the BAs as well as the grid. This study details the behavioural designing of BA that incorporates the functioning above. The proposed architecture also uses an event trigger approach to initiate grid organisation, which is showcased by case studies on IEEE 33 bus system. The results showcase the efficiency of the concept regarding solution accuracy with distributed computations; computational efficiency during contingencies; architecture performance under communication latency; and fault‐tolerant characteristics of the proposed architecture.
Journal Article
Hosting a community‐based local electricity market in a residential network
This paper presents the potential of building a local electricity market (LEM) to boost the deployment of the local energy communities, centred around active customers with distributed energy resources (DERs). To conduct a comprehensive and detailed study on different cases with reduced computational burdens, this paper adopts a simplified modelling approach where the market and network model simulations are performed in a cascaded, decoupled fashion. This allows achieving the optimal LEM output for the energy community with different DER assets that are not bounded by the network constraints. The investigation involves quantifying the benefits brought by LEM to energy communities by tapping the flexibility associated with trading inside the energy community. Moreover, it presents the influence of different types of DERs (mainly photovoltaics (PV) and energy storage (ES)) in customers' premises on the outcome of the LEM. The LEM demonstrates successfully the reduction of cost associated with the energy purchased from the energy retailer and maximises the consumption of locally generated clean electricity. Among the studied DER portfolios, the combination of PV and ES solution shows the highest economic potential but deteriorates the voltage profiles and shows high active power loss in the winter month among all the cases examined.
Journal Article
Estimation of Impedance and Susceptance Parameters of a 3-Phase Cable System Using PMU Data
This paper proposes a new regression-based method to estimate resistance, reactance, and susceptance parameters of a 3-phase cable segment using phasor measurement unit (PMU) data. The novelty of this method is that it gives accurate parameter estimates in the presence of unknown bias errors in the measurements. Bias errors are fixed errors present in the measurement equipment and have been neglected in previous such attempts of estimating parameters of a 3-phase line or cable segment. In power system networks, the sensors used for current and voltage measurements have inherent magnitude and phase errors whose measurements need to be corrected using calibrated correction coefficients. Neglecting or using wrong error correction coefficients causes fixed bias errors in the measured current and voltage signals. Measured current and voltage signals at different time instances are the variables in the regression model used to estimate the cable parameters. Thus, the bias errors in the sensors become fixed errors in the variables. This error in variables leads to inaccuracy in the estimated parameters. To avoid this, the proposed method uses a new regression model using extra parameters which facilitate the modeling of present but unknown bias errors in the measurement system. These added parameters account for the errors present in the non- or wrongly calibrated sensors. Apart from the measurement bias, random measurement errors also contribute to the total uncertainty of the estimated parameters. This paper also presents and compares methods to estimate the total uncertainty in the estimated parameters caused by the bias and random errors present in the measurement system. Results from simulation-based and laboratory experiments are presented to show the efficacy of the proposed method. A discussion about analyzing the obtained results is also presented.
Journal Article