Electric vehicles (EVs) play a crucial role in the transition towards sustainable modes of transportation and thus are critical to the energy transition. As their number grows, managing the aggregate power of EV charging is crucial to maintain grid stability and mitigate congestion. This study analyses more than 500 thousand real charging transactions in the Netherlands to explore the challenge and opportunity for the energy system presented by EV growth and smart charging flexibility. Specifically, it analyses the collective ability to provide congestion management services according to the specifications of those services in the Netherlands. In this study, a data-driven model of charging behaviour is created to explore the implications of delivering dependable congestion management services at various aggregation levels and types of service. The probability of offering specific grid services by different categories of charging stations (CS) is analysed. These probabilities can help EV aggregators, such as charging point operators, make informed decisions about offering congestion mitigation products per relevant regulations and distribution system operators to assess their potential. The ability to offer different flexibility products, namely redispatch and capacity limitation, for congestion management, is assessed using various dispatch strategies. Next, machine learning models are used to predict the probability of CSs being able to deliver these products, accounting for uncertainties. Results indicate that residential charging locations have significant potential to provide both products during evening peak hours. While shared EVs offer better certainty regarding arrival and departure times, their small fleet size currently restricts their ability to meet the minimum order size of flexible products. The findings demonstrate that the timing of EV arrivals, departures, and connections plays a crucial role in determining the feasibility of product offerings, and dependable services can generally be delivered using a sufficiently large number of CSs. @en

Smart charging of electric vehicles can alleviate grid congestion and reduce charging costs. However, various electric vehicle models currently lack the technical capabilities to effectively implement smart charging since they cannot handle charging pauses or delays. These models enter sleep mode when charging is interrupted, preventing resumption afterwards. To avoid this, they should be continuously charged with their minimum charging power, even when a charging pause would be desirable, for instance with high electricity prices. This research examines this problem to inform various stakeholders, including policymakers and manufacturers, and stimulates the adoption of proactive measures that address this problem. Here, we demonstrate through technical charging tests that around one-third of tested car models suffer from this issue. Through model simulations we indicate that eliminating paused and delayed charging problems would double the smart charging potential for all applications. Lastly, we propose concrete legal and practical solutions to eliminate these problems.

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Aggregate peak Electric Vehicle (EV) charging demand is a matter of growing concern for network operators as it severely limits the network's capacity, preventing its reliable operation. Various tariff schemes have been proposed to limit peak demand by incentivizing flexible asset users to shift their demand from peak periods. However, fewer studies quantify the effect of these tariff schemes on the aggregate level. In this paper, we compare the effect of a multi-level segmented network tariff with and without dynamic energy prices for individual EV users on the aggregate peak demand. Results based on real charging transactions from over 1200 public charging points in the Netherlands show that the segmented network tariff with flat energy prices results in more diverse load profiles with increasing aggregation, as compared to cost-optimized dispatch based on only dynamic day-ahead energy prices. When paired with dynamic energy prices, the segmented tariff still outperforms only dynamic energy price-based tariffs in reducing peaks. Results show that a balance between power thresholds and price per threshold is crucial in designing a suitable tariff, taking into account the needs of the power network. We also provide valuable insights to network operators by calculating the diversity factor for various peak demands per charging point.

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Aggregation is crucial to the effective use of flexibility, especially in the case of electric vehicles (EVs) because of their limited individual battery sizes and large aggregate impact. This research proposes a novel method to quantify and represent the aggregate charging flexibility of EV fleets within a fixed flexibility request window. These windows can be chosen based on relevant network operator needs, such as evening congestion periods. The proposed representation is independent of the number of assets but scales only with the number of discrete time steps in the chosen window. The representation involves 2T parameters, with T being the number of consecutive time steps in the window. The feasibility of aggregate power signals can be checked using 2T constraints and optimized using 2(2^T−1) constraints, both exactly capturing the flexibility region. Using a request window eliminates uncertainty related to EV arrival and departure times outside the window. We present the necessary theoretical framework for our proposed methods and outline steps for transitioning between representations. Additionally, we compare the computational efficiency of the proposed method with the common direct aggregation method, where individual EV constraints are concatenated.

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Traction power networks can significantly influence a country's national grid due to their significant power consumption and numerous coupling points. To modernise the ageing Dutch traction power networks and enhance their impact on the utility grid, this study explores practical and cost-effective approaches for upgrading existing 1.5 kV DC traction substations (TS) in the Netherlands into 3 kV bi-directional DC TS. After evaluating the benefits of a 3 kV bi-directional DC, two novel topologies are proposed that re-use the existing substation's components and reduce the need for higher investments. These topologies incorporate parallel voltage source converters (VSCs) to recuperate braking energy from the DC grid and transfer it back to the AC grid. Furthermore, the study investigates additional use cases for the VSCs, including improving DC TS's reliability during faults, reducing harmonics through active power filtering, compensating for reactive power, and supporting the integration of renewable energy sources into the DC grid. A comprehensive control strategy for the VSCs is also proposed based on a thorough analysis of their working methodology and functional modes. The feasibility and effectiveness of the proposed solutions are validated through scenario analysis relevant to the Netherlands' traction network, utilising both a Simulink model and an Opal-RT real-time simulator. This study serves as a starting point for the various stakeholders of the Dutch traction network in their journey towards modernising the current traction power supply. It has the potential to serve as a reference for reusing existing railway infrastructures to provide ancillary services and support the energy transition.@en

A photovoltaic (PV)-rich low-voltage (LV) distribution network poses a limit on the export power of PVs due to the voltage magnitude constraints. By defining a customer export limit, switching off the PV inverters can be avoided, and thus reducing power curtailment. Based on this, this paper proposes a mixed-integer nonlinear programming (MINLP) model to define such optimal customer export. The MINLP model aims to minimize the total PV power curtailment while considering the technical operation of the distribution network. First, a nonlinear mathematical formulation is presented. Then, a new set of linearizations approximating the Euclidean norm is introduced to turn the MINLP model into an MILP formulation that can be solved with reasonable computational effort. An extension to consider multiple stochastic scenarios is also presented. The proposed model has been tested in a real LV distribution network using smart meter measurements and irradiance profiles from a case study in the Netherlands. To assess the quality of the solution provided by the proposed MILP model, Monte Carlo simulations are executed in OpenDSS, while an error assessment between the original MINLP and the approximated MILP model has been conducted.@en

With a growing share of electric vehicles (EVs) in our distribution grids, the need for smart charging becomes indispensable to minimise grid reinforcement. To circumvent the associated capacity limitations, this paper evaluates the effectiveness of different levels of network constraints and different dynamic tariffs, including a dynamic network tariff. A detailed optimisation model is first developed for public charging electric vehicles in a representative Dutch low voltage (LV) distribution network, susceptible to congestion and voltage problems by 2050 without smart charging of EVs. Later, a detailed reflection is made to assess the influence of the modelled features on the distribution system operator (DSO), charge point operator (CPO) costs, and the EVs' final state-of-charge (SOC) for both mono- (V1G) and bi-directional (V2G) charging. Results show that the dynamic network tariff outperforms other flat tariffs by increasing valley-filling. Consequently, compared to regular day-ahead pricing, a significant reduction in the frequency of congestion in the lines is achieved. In addition, V2G ensures the joint optimum for different stakeholders causing adequate EV user satisfaction, decreased CPO costs compared to conventional charging and fewer violations of grid constraints for the DSOs.

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The widening gap between energy generation and demand on a global scale, coupled with the imperative to reduce emissions, has necessitated the development of largescale sustainable energy solutions. Among the various renewable energy options, Wind Power stands out as a viable source capable of generating substantial amounts of electricity. However, the unpredictable nature of wind availability and its fluctuations pose challenges for grid operators in effectively harnessing and distributing the generated wind power. This issue becomes more pronounced when transmitting wind power through local grids to distant load centers. Voltage instability at local buses emerges as a significant concern in wind-integrated power systems. To address these challenges, dynamic compensation at multiple locations has proven to be an effective solution. Various alternative approach to controlling the firing of Static Var Compensators (SVCs) connected to the network is proposed in the present work. The traditional method, which relies on a classical control approach, is computationally intensive and time-consuming. To overcome this limitation, we propose the utilization of a trained Neural Network for simultaneous control of the firing angles of all SVCs, accommodating various system conditions such as change in load and wind generation fluctuations. Porposed method has been evaluated on both a modified IEEE-30 bus system and a 28-bus Indian system.

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Demand-side management (DSM) is an effective way to strengthen the present power system's reliability and security with increasing penetration of renewable energy generations. With the fusion of information technology, present-day loads are getting smarter with their ability to modulate the power and control their switching operations in response to signals. The benefits get multiplied when flexibility is planned for a cluster of consumers having a similar load profile. In this paper, a framework based on mixed-integer linear programming (MILP) is developed to quantify flexibility in a large business park with little historical time series data access. The proposed mathematical model considers smart loads such as heat pumps, electric vehicle (EV) charging stations, a centralized energy storage system and renewable energy sources such as photovoltaic power. The quantification of flexibility is cast as a bi-objective optimization problem, which is solved by approximating the set of Pareto-efficient solutions using the epsilon-constraint method. Based on the developed optimization model, numerical simulations across one year with a time step of one hour are performed. The projected yearly monetary saving ranges from 1.5% to 30.8 %, and maximum peak shavings range from 9.6% to 61.4% for different capacities of centralized energy storage.@en

Sine pulse width modulation is one technology used mostly in power inverters nowadays to reduce bulky filter requirements and give a pure sinusoidal wave. This paper brings forward a novel stand-alone sine-wave inverter utilizing sine PWM technology in a full-bridge inverter with a modified topology having two additional buck switches connected at the output of a conventional H-bridge topology. This inverter totally deals with six switches from which the two additional switches are the only ones operating at high frequency while the other switches operate at the low (line) frequency. This improves the voltage control and improves the overall efficiency by reducing switching loss. The above topology aims to reduce the switching losses by half as compared to a standard H-bridge hence increasing the efficiency as well as increasing the reliability of the high switching switches as they operate alternatively for only half cycle. The entire idea was simulated and verification of the same was done in laboratory utilizing a prototype model.

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Information and Communication Technology(ICT) is the main impetus of the new age smart grids. The two-way communication between the utility and consumer is the most important aspect which makes the grid 'smart.' Energy monitoring systems play a vital role in the evolution of the Smart grid. This paper proposes an XBee based non-invasive smart energy monitoring system which is highly scalable and robust. The proposed system collects real-time data accurately from every node using a compact and portable setup which can be easily installed superficially using a clamp. It offers an IoT based platform for the consumers and the utility where they can monitor and interact with each other. The proposed design is more secure The proposed system design has an accuracy level of 97% and it is about 30% lower cost than its peer in the global market. The proposed design reduces the power consumption by 18% by the use of a pre-paid tariff system. Further, a hardware setup is developed to test the validity and compare the performance of the proposed design.

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An efficient and innovative method has been proposed in this paper to detect heart murmurs as a method to identify rheumatic fever with the use of adaptive filters, transform techniques and Neural Network Algorithms by considering various parameters such as number of peaks, Signal to Noise Ratio (SNR) and Power Spectral Density. Under optimum conditions the classification returned exact outputs even when the neural network was trained under false positive data thus showing its effectiveness.@en

Internet of Things (IOT) is a development of the internet which plays a major role in integrating human-machine interaction by allowing everyday objects to send and receive data in a variety of applications. Using IOT in healthcare monitoring provides an avenue for doctors and patients to interact and to track the dosage of medication administered. The paper presents an interactive, user friendly network integrated with an automated medicine dispenser which uses IOT, cloud computing and machine learning. The network was built on a python tornado framework with a front end developed using materialise CSS. The feasibility of this approach was validated by building a prototype and conducting a survey.

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