Transmission System Operators Research Articles

Blockchain based sustainable energy transition of a Virtual Power Plant: Conceptual framework design & experimental implementation

Sustainable frameworks are being introduced by technological breakthroughs to address the continuously increasing demand for energy and this article presents a novel framework for a Virtual Power Plants to sustain and optimize Distributed Energy Resources (DERs) administration through blockchain technology. The multi-layered architecture integrates blockchain technology with other VPP components, such as a cloud service provider, grid and transmission operator, and forecasting of DERs, to optimize bidding and scheduling in real-time across multiple markets to enhance energy efficiency and grid stability. By employing a system engineering methodology, this framework incorporates smart meters to facilitate the transmission and reception of data i.e., voltage, and current between participants, and DERs within the network. This strategy makes it easier to advance sustainable energy sources, which advances predictive capabilities, strengthens the framework’s resilience, and protects the environment. The investigation of demand response, functional and system trade-off analysis, configuration selection, and other subsystems within a decentralized network offers insights into the necessary components and challenges involved in the construction of such a system. This study contributes comprehensive assistance and valuable insights to both practitioners and researchers involved in the field of sustainable energy transition through Virtual Power Plant.

Life Cycle Analysis of a Battery Energy Storage System

For Latvian government and transmission system operator energy security and independence has been an issue for a period of time, even more when political situation changed, motivating to act faster in order to swich off from BREL system completely. Therefore, three Baltic states have made an agreement on building more stable electricity transmission system within Baltic region and to connect with European transmission systems, building new stations with synchronous compensators and energy storage systems. In order to build a system, that allows to include more energy from renewable resources and secure possible disbalances in system, Latvian TSO operator is building a new battery energy storage system. This paper is aimed to look at what are the costs of this battery energy storage system from climate perspective to Latvia and its society in order to gain energy security and possibility to include more energy from renewable resources.

The role of data-driven methods in power system security assessment from aggregated grid data

Data-driven techniques have been considered as an enabling technology for reducing the computational burden of both static and dynamic power system security analysis. Anyway, the studies reported in the literature mainly focused on inferring from historical data the mapping between the bus variables before and after a certain contingencies set, while, to the best of the Author's knowledge, limited contributions have been devoted to try and classify the power system security state by processing aggregated grid data. This is a relevant issue to address for a Transmission System Operator since it could allow a sensible decrease in the computational burden and, considering that aggregated grid data can be reliably predicted from several hours to one day ahead, it may enable the evolution of security assessment to security forecasting. In trying and filling this research gap, this paper explores the role of machine learning and feature selection algorithms. A realistic case study involving 2 years of synthetic grid data simulated on the Italian power system model against future potential operational scenarios characterized by a high share of renewables is presented and discussed to identify the most promising computing paradigms, analyzing the criticality of tuning the feature selection and classifier algorithms.

A Survey on Data Compression Techniques for Automotive LiDAR Point Clouds.

In the evolving landscape of autonomous driving technology, Light Detection and Ranging (LiDAR) sensors have emerged as a pivotal instrument for enhancing environmental perception. They can offer precise, high-resolution, real-time 3D representations around a vehicle, and the ability for long-range measurements under low-light conditions. However, these advantages come at the cost of the large volume of data generated by the sensor, leading to several challenges in transmission, processing, and storage operations, which can be currently mitigated by employing data compression techniques to the point cloud. This article presents a survey of existing methods used to compress point cloud data for automotive LiDAR sensors. It presents a comprehensive taxonomy that categorizes these approaches into four main groups, comparing and discussing them across several important metrics.

Getting Ready for Multi-Vendor and Multi-Terminal HVDC Technology

Interoperable multi-vendor High-Voltage Direct-Current (HVDC) grids are a key enabler for the integration of renewable energy (in particular offshore wind) and its transmission over longer distances to consumers. However, most HVDC systems today are single-vendor and point-to-point. Various technical and non-technical aspects need to be considered, for example, (real-time) testing, legal aspects (intellectual property and regulation), and the multi-vendor interoperability process. This paper presents findings from the READY4DC project, which is a larger and open European effort involving diverse stakeholders, including HVDC manufacturers, transmission system operators, wind developers, academia, and research institutes. It summarizes key technical recommendations, emphasizing comprehensive interaction studies and the development of a structured legal framework to facilitate the development and operation of a multi-vendor, multi-terminal HVDC grid. The READY4DC project highlights the need for increased harmonization, transparent communication among stakeholders, and future-oriented research to ensure the robustness and interoperability of interconnected grids. Collaborative efforts are key for addressing technical complexities and advancing the deployment of multi-vendor multi-terminal HVDC technology.

Experimental and Finite Element Analyses of Adjustable Foundation Bolts in Transmission Towers

Uneven settlement of transmission tower foundations can result in catastrophic events, such as tower collapse and line failures, disrupting power transmission operations. To address the challenging repairs caused by uneven foundation settlement of transmission towers, we propose an adjustable foundation bolt (AFB). This paper provides a detailed theoretical analysis of the AFB’s stability and load-bearing capacity, including critical buckling force formulas and maximum normal stress expressions. Finite element simulations confirm the precision of our theoretical formulations. Additionally, we introduce a method using baffles to enhance its load-bearing capacity, analyzing the impact of different numbers of baffles through numerical simulations. The experimental results validate the effectiveness of baffles in enhancing structural load-bearing capacity. The device brings convenience and efficiency to the maintenance of transmission towers.

An energy management system to schedule the optimal participation to electricity markets and a statistical analysis of the bidding strategies over long time horizons

This paper presents a tool that allows to perform an ex-ante estimation of the profitability to participate to the electric energy and service markets for a microgrid or a polygeneration power plant. The tool is the evolution of an Energy Management System (EMS) previously developed by the University of Genoa and Renantis Solutions Srl. Constraints are added to the original EMS to model the Ancillary Service Market (ASM) and Day Ahead Market (DAM) mechanisms in the framework of a planning tool that allows to get estimates of the microgrid/plant costs and revenues over a long-time horizon. As the market outcome strongly depends on the Transmission System Operator (TSO) acceptance/rejection of bids and offers, a statistical approach is proposed. The optimization process is split in two steps: the first one optimally dispatches the energy units and proposes DAM offers and ASM bid and offers over a specified time horizon. Then, a Monte Carlo method is launched; at each extraction the TSO acceptance/rejection of each ASM bid and offer is simulated and there is a second step of optimization in which the energy sources are optimally re-dispatched to meet the bidding program and so to avoid imbalance power exchange that would increase the energy cost. The cost/revenue is thus computed at each extraction. The result is the probability density function of the costs/revenues that allows to estimate the profitability of the participation to the electricity markets. One test performed on a real microgrid shows that the computational time needed to complete one iteration related to a time horizon of one month is approximately 20 s. Moreover, the comparison of the outcomes with two independent launches to define the set of bids and offers to be accepted in ASM demonstrates that the statistical analysis allows to avoid misleading conclusions on the costs/revenues. Consequently, the fact that the developed EMS allows to perform a statistical analysis in a reasonable time enables the possibility of comparing multiple scenarios and gives an indication related to the range of cost/revenues in which the microgrid/plant may incur. Finally, the computational efficiency of the proposed approach allows to conclude that the developed EMS is appropriate to perform a long-time horizon (of the order of magnitude of years) analysis and, at the same time, allows to model all the involved components with a high level of details.

Controlling the broadband enhanced light chirality with L-shaped dielectric metamaterials

The inherently weak chiroptical responses of natural materials limit their usage for controlling and enhancing chiral light-matter interactions. Recently, several nanostructures with subwavelength scale dimensions were demonstrated, mainly due to the advent of nanofabrication technologies, as a potential alternative to efficiently enhance chirality. However, the intrinsic lossy nature of metals and the inherent narrowband response of dielectric planar thin films or metasurface structures pose severe limitations toward the practical realization of broadband and tailorable chiral systems. Here, we tackle these problems by designing all-dielectric silicon-based L-shaped optical metamaterials based on tilted nanopillars that exhibit broadband and enhanced chiroptical response in transmission operation. We use an emerging bottom-up fabrication approach, named glancing angle deposition, to assemble these dielectric metamaterials on a wafer scale. The reported strong chirality and optical anisotropic properties are controllable in terms of both amplitude and operating frequency by simply varying the shape and dimensions of the nanopillars. The presented nanostructures can be used in a plethora of emerging nanophotonic applications, such as chiral sensors, polarization filters, and spin-locked nanowaveguides.

Aggregated model of an active distribution system for coordinated TSO-DSO fast frequency control

The growing integration of distributed energy sources (DERs) challenges the traditional independent operation of transmission system operators (TSOs) and distribution system operators (DSOs). The coordination between TSOs and DSOs becomes essential, as DERs not only offer local ancillary services but also enable the provision of these services at the distribution network level following disturbances in the transmission system. To enhance power system analyses in TSO-DSO coordination, the availability of reduced-order or aggregated models proves crucial, avoiding detailed modeling and reducing computational burden. Addressing the lack of reduced-order models capturing the dynamics of diverse distributed technologies and their control systems within a unified single-device model, this paper introduces a single equivalent device based on virtual synchronous machine (VSM) technology. This device effectively subsumes the frequency response of various DERs and loads. The proposed aggregation methodology undergoes validation on systems of different sizes and compositions of DERs. Results from simulated scenarios consistently demonstrate the accuracy and generalizability of the proposed aggregated model, irrespective of system size and included technologies.

Security constrained economic dispatch with VSC-HVDC connected wind farms

The rise in wind power in electrical networks creates complex challenges for transmission system operators (TSOs), particularly regarding real-time operation. TSOs rely on grid codes to enforce wind farm (WF) participation in primary frequency control (PFC), like conventional plants. This paper addresses this need by proposing a security constrained economic dispatch (SCED) model with VSC-HVDC connected WFs based on variable speed wind generators. A key differentiator from existing models is the explicit calculation of the spinning reserve and the droop parameter of the WFs. This method uses the deloading schemes related to blade pitch angle and mechanical rotor speed to ensure compliance with grid codes related to PFC. We show that the wind speed and these deloading schemes significantly impact the WF droop parameter. For efficient studies, linear shift factors are used to model the transmission grid and the VSC-HVDC connected WFs. The performance of the SCED model was assessed using two standard test systems that incorporate WFs connected by VSC-HVDC lines. Its effectiveness was confirmed by comparing it with the security constrained optimal power flow (SCOPF). The results showed agreement between both models, with differences less than 3 % and with the SCED saving about 70 % of the computing time.

Centralized Protection and Control for Transmission System Operations: Practical Applications and Perspectives

Centralized Protection and Control for Transmission System Operations: Practical Applications and Perspectives

Nonlinear dynamics analysis of electromechanical planetary gear systems with temperature effects considered

PurposeThe electromechanical planetary transmission system has the advantages of high transmission power and fast running speed, which is one of the important development directions in the future. However, during the operation of the electromechanical planetary transmission system, friction and other factors will lead to an increase in gear temperature and thermal deformation, which will affect the transmission performance of the system, and it is of great significance to study the influence of the temperature effect on the nonlinear dynamics of the electromechanical planetary system.Design/methodology/approachThe effects of temperature change, motor speed, time-varying meshing stiffness, meshing damping ratio and error amplitude on the nonlinear dynamic characteristics of electromechanical planetary systems are studied by using bifurcation diagrams, time-domain diagrams, phase diagrams, Poincaré cross-sectional diagrams, spectra, etc.FindingsThe results show that when the temperature rise is less than 70 °C, the system will exhibit chaotic motion. When the motor speed is greater than 900r/min, the system enters a chaotic state. The changes in time-varying meshing stiffness, meshing damping ratio, and error amplitude will also make the system exhibit abundant bifurcation characteristics.Originality/valueBased on the principle of thermal deformation, taking into account the temperature effect and nonlinear parameters, including time-varying meshing stiffness and tooth side clearance as well as comprehensive errors, a dynamic model of the electromechanical planetary gear system was established.

Common TSO-DSO market framework with no upfront priority to utilize DER flexibility

Grid integration of distributed energy resources (DERs) is vital to provide flexibility services, required to ensure efficient system operations at both transmission and distribution levels. To prevent negative interactions between transmission system operators (TSO) and distribution system operators (DSOs) while procuring flexibility services from DERs, it is imperative to prioritize them. However, for system's cost-effectiveness allocating DER flexibility to the operator with the highest need is preferable rather than giving upfront priority. To address this, a novel coordinated decision-making approach for a common TSO-DSO market is proposed. This approach considers global balancing for the TSO and distribution congestion management for the DSO as objectives, using them as deciding factor to allocate DER flexibility to the system operator with the greatest need to fulfil its objective. Results show proposed method enhances DSO's flexibility transfer to TSO and maintains reactive power at T-D interface. Gap between TSO's DER flexibility needs and availability, influenced by congestion and line losses, is critical for prioritization. Specifically, network capacity and line losses constitute 18.79 % and 3.42 % of offered DER flexibility, respectively. Reactive power deployment improves Q/P ratio, reducing standard deviation from 0.19 to 0.018. Framework reduces conflicts, provides clear flexibility allocation rules without upfront priority.

Cost minimization of a hybrid PV-to-methanol plant through participation in reserve markets: A Danish case study

In efforts to combat carbon emissions in challenging sectors like heavy industry and shipping, Power-to-X (PtX) technology emerges as a promising solution. PtX involves converting renewable energy into hydrogen via water electrolysis and further processing it into eco-friendly fuels like methanol, ammonia, or kerosene. Denmark has seen a rise in plans for integrating large-scale PtX projects, which will introduce large electrical loads to its power system.To optimize the cost-effectiveness of PtX-based fuel production, a proposal suggests leveraging PtX plants’ inherent flexibility to provide frequency reserves to the electrical grid. The study centers on a real-life hybrid renewable energy system (HRES) dedicated to methanol production in Denmark.Analyzing the HRES technology and current reserve markets reveals that the HRES qualifies for frequency reserve provision certification, encompassing FCR, aFRR, and mFRR, by the transmission system operator. Through participation in the reserve market, the HRES can generate added revenue, resulting in substantial reductions of 12% and 30% in the levelised cost of methanol for 2020 and 2021 operational expenditure outcomes, respectively.The study illustrates significant cost reductions achievable through engagement in reserve markets, and these reductions may escalate further with increased market prices and volatility. Participating in frequency reserve capacity markets not only facilitates overall cost reduction but also serves as an insurance policy against unpredictable energy market fluctuations. This approach presents a promising trajectory to promote PtX technology adoption and nurture sustainable energy practices in challenging sectors.

Hierarchical optimal dispatching method of transmission system operators‐distribution system operators cooperated integrated energy system considering gas‐thermal equivalence model

AbstractThe integrated energy system (IES) is moving towards the large‐scale cooperation between transmission system operators (TSO) and distribution system operators (DSO) with the coupling of electricity, heating, and gas networks. The conventional optimization method is not time‐efficient for solving such a large‐scale problem when taking account of the cooperation of TSO‐DSO of the IES. To solve such a problem, this paper proposed a hierarchical optimization framework for TSO‐DSO cooperated IES. To further improve the computation efficiency, the equivalent models of heating and gas system that retain the heating inertia characteristic and gas pipe storage characteristic are presented. A computation method for the TSO‐DSO cooperated IES is also proposed from the hierarchical distributed optimization perspective. The case study confirms the efficacy of the proposed gas‐thermal equivalent model in significantly enhancing computational speed. Furthermore, the proven effectiveness of the hierarchical distributed optimization approach in leveraging the full potential of flexible resources in transmission and distribution systems while safeguarding internal information privacy enables the development of robust dispatching strategies for each system.

Coupling energy management of power systems with energy hubs through TSO-DSO coordination: a review

Abstract The transition towards a sustainable energy system requires the integration of high-penetration renewable energy sources (RESs) and the use of new energy management frameworks to handle RES variability and uncertainty. The evolution of novel energy management technologies has made it possible to design and operate integrated energy systems (IESs) that couple various energy carriers such as electricity, heat, and gas. The emergence of energy hubs (EHs) offers the potential for greater efficiency, reliability, and flexibility in energy supply and demand. However, integrating EHs into the power system operation remains a significant challenge due to the complex interaction between the transmission system operator (TSO), distribution system operator (DSO), and EHs. Enhanced coordination between TSOs and DSOs is of the utmost importance within this context. This paper first aims to review and highlight the most practical features of EHs including the EH concept, topology, elements, classifications, flexibility resources, and optimization approaches. Subsequently, it describes TSO-DSO roles and all concepts that are in line with the aims of this subject, such as conceptual framework, TSO-DSO coordination schemes, formulation, and solution techniques. Meanwhile, it will identify any remaining research gaps and prospective trends in this respect.

Replacing Fossil-Fueled Combined Heat and Power Plants with Malta’s Pumped Heat Energy Storage Technology to Provide Clean Power and District Heat

This study analyzes the potential integration of a 100 MWel, 36-hour Malta Pumped Heat Energy Storage (PHES) system into the district heating network of the city of Hamburg, Germany, using energy from a nearby offshore wind farm that would otherwise be curtailed to charge the system. Publicly available data showing the times when curtailment instructions were given by the transmission system operator were used to determine the hours during which the storage system should be charged. Malta’s proprietary hourly performance model was used to simulate the behavior and performance of different plant configurations. It was shown that this configuration could avoid the curtailment of 227 GWhel of wind energy per year. The study showed that the system could provide 117 GWhel of electricity to the grid, as well as 72 GWhth of thermal energy for Hamburg’s district heating network, during periods where less renewable energy was available. This system could reduce the annual CO2 emissions by 101,400 tonnes compared to the coal-fired combined heat and power (CHP) plant that it would replace.

Edge effects in quantum cellular automata binary wire

The molecular quantum cellular automata approach provides a paradigm for molecular electronics, where the charge configurations at reduction–oxidation centers of molecules represent binary information. The QCA cells are coupled by electrostatic fields and enable implementing devices for digital data transmission and logic operation at the molecular scale. Designing inputs for the desired operation of molecular QCA devices has been a challenging task, where the edge effects of electrodes may reduce the reliability of the QCA devices. We calculate the fringing fields of a pair of electrodes implemented on a model platform for the operation of a molecular QCA binary wire. The calculated nanogap and fringing fields are used in a simple quantum mechanical model to analyze the edge effects on the polarization propagation and polarization loss throughout the wire. Discussions on the stability of the wire in the presence of the fringing fields using the excited state of the wire and polarization uncertainties are presented.

Machine Learning Algorithms for Power System Sign Classification and a Multivariate Stacked LSTM Model for Predicting the Electricity Imbalance Volume

The energy transition to a cleaner environment has been a concern for many researchers and policy makers, as well as communities and non-governmental organizations. The effects of climate change are evident, temperatures everywhere in the world are getting higher and violent weather phenomena are more frequent, requiring clear and firm pro-environmental measures. Thus, we will discuss the energy transition and the support provided by artificial intelligence (AI) applications to achieve a cleaner and healthier environment. The focus will be on applications driving the energy transition, the significant role of AI, and collective efforts to improve societal interactions and living standards. The price of electricity is included in almost all goods and services and should be affordable for the sustainable development of economies. Therefore, it is important to model, anticipate and understand the trend of electricity markets. The electricity price includes an imbalance component which is the difference between notifications and real-time operation. Ideally it is zero, but in real operation such differences are normal due to load variation, lack of renewable energy sources (RES) accurate prediction, unplanted outages, etc. Therefore, additional energy has to be produced or some generating units are required to reduce generation to balance the power system. Usually, this activity is performed on the balancing market (BM) by the transmission system operator (TSO) that gathers offers from generators to gradually reduce or increase the output. Therefore, the prediction of the imbalance volume along with the prices for deficit and surplus is of paramount importance for producers’ decision makers to create offers on the BM. The main goal is to predict the imbalance volume and minimize the costs that such imbalance may cause. In this chapter, we propose a method to predict the imbalance volume based on the classification of the imbalance sign that is inserted into the dataset for predicting the imbalance volume. The imbalance sign is predicted using several classifiers and the output of the classification is added to the input dataset. The rest of the exogenous variables are shifted to the values from previous day d − 1. Therefore, the input variables are either predicted (like the imbalance sign) or are known from d − 1. Several metrics, such as mean average percentage error (MAPE), determination coefficient R2 and mean average error (MAE) are calculated to assess the proposed method of combining classification machine learning (ML) algorithms and recurrent neural networks (RNN) that memorize variations, namely long short-term memory (LSTM) model.

On the Robustness of Channel Allocation in Joint Radar and Communication Systems: An Auction Approach

Joint radar and communication (JRC) is a promising technique for spectrum re-utilization, which enables radar sensing and data transmission to operate on the same frequencies and the same devices. However, due to the multi-objective property of JRC systems, channel allocation to JRC nodes should be carefully designed to maximize system performance. Additionally, because of the broadcast nature of wireless signals, a watchful adversary, i.e., a warden, can detect ongoing transmissions and attack the system. Thus, we develop a covert JRC system that minimizes the detection probability by wardens, in which friendly jammers are deployed to improve the covertness of the JRC nodes during radar sensing and data transmission operations. Furthermore, we propose a robust multi-item auction design for channel allocation for such a JRC system that considers the uncertainty in bids. The proposed auction mechanism achieves the properties of truthfulness, individual rationality, budget feasibility, and computational efficiency. The simulations clearly show the benefits of our design to support covert JRC systems and to provide incentive to the JRC nodes in obtaining spectrum, in which the auction-based channel allocation mechanism is robust against perturbations in the bids, which is highly effective for JRC nodes working in uncertain environments.