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.

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.

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.

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.

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.

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.

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.

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.