Virtual power plants: challenges, opportunities, and profitability assessment in current energy markets

The arrival of virtual power plants (VPPs) marks important progress in the energy sector, providing optimistic solutions to the increasing need for energy flexibility, resilience, and improved energy systems’ integration. VPPs harness several characteristics to bring together distributed energy resources (DERs), resulting in economic gains and improved power grid reliability. Nevertheless, VPPs encounter major challenges when it comes to engaging in energy markets, mainly because there is no all-encompassing policy and regulatory framework specifically designed to accommodate their unique characteristics. This underscores the necessity for research endeavours to develop more advanced methods and structures for the long-term viability of VPPs. To address this concern, the study advocates for the implementation of a multi-aspect framework (MAF) as a systematic approach to thoroughly examine each aspect of virtual power plants (VPPs). A STEEP (social, technological, environmental, economic, and political) analytical tool is utilized to evaluate the challenges, opportunities, and benefits of a VPP in the existing energy markets. The proposed approach highlights important factors and actions that need to be taken to tackle the challenges related to VPP’ entry into energy markets. This study suggests that further support is required to promote the fast and widespread adoption of long-term VPP implementations. For this reason, a more favourable policy and regulatory framework based on social, technological, economic, environmental, and policy considerations is necessary to realize the genuine contributions of VPPs.</p

Monitoring insulator contamination level under dry condition with a microwave reflectometer

—Build-up of surface contamination on high voltage insulators can lead to an increase in leakage current and partial discharge, which may eventually develop into flashover. Conventional contamination level monitoring systems based on leakage current, partial discharge, infrared and ultraviolet camera are only effective when the contamination layer has been wetted by rain, fog or condensation; under these conditions flashover might occur before there is time to implement remedial measures such as cleaning. This paper describes studies exploring the feasibility of applying microwave reflectometry techniques to monitor insulator contamination levels. This novel method measures the power generated by a 10.45 GHz source and reflected at the insulator contamination layer. A theoretical model establishes the relationship between equivalent salt deposit density (ESDD) levels, dielectric properties and geometry of contamination layers. Experimental results demonstrate that the output from the reflectometer is able to clearly distinguish between samples with different contamination levels under dry conditions. This contamination monitoring method could potentially provide advance warning of the future failure of wet insulators in climates where insulators can experience dry conditions for extended periods

Influence of single and multiple dry bands on critical flashover voltage of silicone rubber outdoor insulators: simulation and experimental study

Dry band formation on the surface of outdoor insulators is one of the main reasons leading to flashover and power outages. In this paper, a dynamic arc model is proposed for single and multiple dry bands configuration to predict the critical flashover voltage for silicone rubber outdoor insulators. An arc is modelled as a time dependent impedance consisting of a Resistor Inductor Capacitor (RLC) circuit. The effect of dry band location and existence of multiple dry bands on critical flashover voltage is investigated. To validate the proposed model, experiments were conducted in a climate chamber under controlled environmental conditions on rectangular silicone rubber sheets polluted using improved solid layer method based on IEC 60,507. Tests were conducted at different dry band configurations and pollution severity levels. A good correlation was found between experimental results and simulation results. This model can provide a good foundation for the development of mathematical models for station post insulators having multiple dry and clean bands and can be used in the design and selection of outdoor insulators for polluted conditions

Towards online ageing detection in transformer oil: a review

Transformers play an essential role in power networks, ensuring that generated power gets to consumers at the safest voltage level. However, they are prone to insulation failure from ageing, which has fatal and economic consequences if left undetected or unattended. Traditional detection methods are based on scheduled maintenance practices that often involve taking samples from in situ transformers and analysing them in laboratories using several techniques. This conventional method exposes the engineer performing the test to hazards, requires specialised training, and does not guarantee reliable results because samples can be contaminated during collection and transportation. This paper reviews the transformer oil types and some traditional ageing detection methods, including breakdown voltage (BDV), spectroscopy, dissolved gas analysis, total acid number, interfacial tension, and corresponding regulating standards. In addition, a review of sensors, technologies to improve the reliability of online ageing detection, and related online transformer ageing systems is covered in this work. A non-destructive online ageing detection method for in situ transformer oil is a better alternative to the traditional offline detection method. Moreover, when combined with the Internet of Things (IoT) and artificial intelligence, a prescriptive maintenance solution emerges, offering more advantages and robustness than offline preventive maintenance approaches

Design of a microwave radiometer for monitoring high voltage insulator contamination level

Microwave radiometry is a novel method for monitoring contamination levels on high voltage insulators. The microwave radiometer described measures energy emitted from the contamination layer and could provide a safe, reliable, contactless monitoring method that is effective under dry conditions. The design of the system has focused on optimizing accuracy, stability and sensitivity using a relatively low cost architecture. Experimental results demonstrate that the output from the radiometer is able to clearly distinguish between samples with different contamination levels under dry conditions. This contamination monitoring method could potentially provide advance warning of the future failure of wet insulators in climates where insulators can experience dry conditions for extended periods

Sensitivity analysis of intensity-modulated plastic optical fiber sensors for effective aging detection in rapeseed transformer oil

As the focus tilts toward online detection methodologies for transformer oil aging, bypassing challenges associated with traditional offline methods, such as sample contamination and misinterpretation, fiber optic sensors are gaining traction due to their compact nature, cost-effectiveness, and resilience to electromagnetic disturbances that are typical in high-voltage environments. This study delves into the sensitivity analysis of intensity-modulated plastic optical fiber sensors. The investigation encompasses key determinants such as the influence of optical source wavelengths, noise response dynamics, ramifications of varying sensing lengths, and repeatability assessments. Our findings highlight that elongating sensing length detrimentally affects both linearity response and repeatability, largely attributed to a diminished resistance to noise. Additionally, the choice of the optical source wavelength proved to be a critical variable in assessing sensor sensitivity

Forecasting Flashover Parameters of Polymeric Insulators under Contaminated Conditions Using the Machine Learning Technique

There is a vital need to understand the flashover process of polymeric insulators for safe and reliable power system operation. This paper provides a rigorous investigation of forecasting the flashover parameters of High Temperature Vulcanized (HTV) silicone rubber based on environmental and polluted conditions using machine learning. The modified solid layer method based on the IEC 60507 standard was utilised to prepare samples in the laboratory. The effect of various factors including Equivalent Salt Deposit Density (ESDD), Non-soluble Salt Deposit Density (NSDD), relative humidity and ambient temperature, were investigated on arc inception voltage, flashover voltage and surface resistance. The experimental results were utilised to engineer a machine learning based intelligent system for predicting the aforementioned flashover parameters. A number of machine learning algorithms such as Artificial Neural Network (ANN), Polynomial Support Vector Machine (PSVM), Gaussian SVM (GSVM), Decision Tree (DT) and Least-Squares Boosting Ensemble (LSBE) were explored in forecasting of the flashover parameters. The prediction accuracy of the model was validated with a number of error cost functions, such as Root Mean Squared Error (RMSE), Normalized RMSE (NRMSE), Mean Absolute Percentage Error (MAPE) and R. For improved prediction accuracy, bootstrapping was used to increase the sample space. The proposed PSVM technique demonstrated the best performance accuracy compared to other machine learning models. The presented machine learning model provides promising results and demonstrates highly accurate prediction of the arc inception voltage, flashover voltage and surface resistance of silicone rubber insulators in various contaminated and humid conditions