Transmission System Operators Research Articles

Co-Optimization of Distributed Renewable Energy and Storage Investment Decisions in a TSO-DSO Coordination Framework

As Distributed Energy Resources (DER) penetration levels and distributed flexibility investments are continuously growing, various smart grid actors need to coordinate their decisions towards optimal DER siting and sizing: First, profit-based Energy Service Providers (ESPs) want to secure their long-term profits and avoid economically unsustainable DER investments. Second, Distribution System Operators (DSOs) need to ensure the reliable operation of their networks in an economically optimal way. Third, Transmission System Operators (TSOs) want to optimally exploit the available “clean” DERs in close collaboration with the downstream DSOs. In this paper, we propose a novel ESP-DSO-TSO coordination scheme to co-optimize distributed renewable energy and storage planning at the distribution network level, while modeling the coordinated TSO-DSO operations. We formulate a bi-level program, the upper-level of which minimizes the DSO's costs, ensuring a minimum rate of return on ESP's investments, while the lower-level models the transmission network-constrained wholesale market. A nested decomposition technique is used to achieve computational tractability. Simulation results showcase a trade-off analysis between sustainable DER investments and system cost minimization and prove that an ESP-DSO-TSO interaction can benefit all involved actors to a certain extent. Finally, the computational efficiency of the proposed nested decomposition algorithm is demonstrated via numerical results.

A Methodology to Evaluate Reactive Power Reserves Scarcity During the Energy Transition

The lack of reserves for reactive power production and absorption is an envisioned, still basically unexplored, threat to the voltage profile adequacy and thereby secure operation of transmission grids during the energy transition toward renewable-dominated power production. This paper proposes a novel, generic, comprehensive, and realistic methodology to identify when this issue of reactive power reserves (RPRs) scarcity during plausible scenarios of the energy transition would become severe. The computational core of the proposed methodology comprises four different AC security-constrained optimal power flow (SCOPF) problems: one conventional, two tailored ones that assess the RPRs scarcity in production and absorption modes, respectively, and an optimal reactive power dispatch. The methodology is versatile, offering the possibility to assess RPRs in different timescales, ranging from day-ahead short-term operation to years-ahead long-term operation, and considering appropriate renewable energy production forecasts and day-dependent load profiles. The proposed methodology can serve as a decision support tool for the transmission system operator (TSO), allowing to plug and play different plausible energy transition scenarios (e.g., differing in terms of sequence and timing of: phased out power plants as well as location, type, and size of renewable energy sources deployed) and ultimately informing the TSO about the timing where RPRs become insufficient to maintain security. Without loss of generality, the value of the proposed methodology is extensively demonstrated in a 60-bus Nordic32 system, considering 52 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N-1$</tex-math></inline-formula> line and generator contingencies, while the tractability of AC SCOPF problems is assessed in a 1,203-bus system.

Research on Step-down Resonance Suppression and Filtering Device

In recent years, offshore wind farms have developed rapidly, and their installed capacity is getting bigger and bigger, and the offshore distance is getting farther and farther. In order to meet the needs of large-capacity and long-distance offshore wind power transmission, the AC submarine cable has the characteristics of long distance, high voltage and large cross-section. Along with the significantly improved transmission capacity, its equivalent capacitance is also increasing, which further reduces the resonance frequency of the offshore wind power AC transmission system. If the parallel resonance frequency of the offshore wind power transmission system is close to the main harmonic current frequency band of wind turbines, it will cause harmful system resonance and harmonic amplification, significantly increasing the harmonic current of grid-connected lines and the voltage distortion rate of nodes in nearby power grids, and seriously affect the safety of equipment and the safe and stable operation of offshore wind power transmission system. A step-down resonance suppression filtering device applied to the offshore wind power transmission system is proposed. The AC cable is connected to the low-voltage 35 kV side of the compensation winding of the centralized control center to eliminate the resonance risk of the 220 kV system and filter out the harmonic current. Compared with the existing technical scheme, the device has the advantages of simple control, reliable operation, stable resonance suppression effect, low operation loss and the like, and has great engineering popularization value.

A platform for demand response and intentional islanding in distribution grids: The LIVING GRID demonstration project

An optimization-based platform is presented to request ancillary services and perform demand response. The work has seen a joint effort of the Italian TSO and DSO (Transmission and Distribution System Operators) and active local prosumers within the LIVING GRID innovation and demonstration project. The platform, a pure software architecture based on existing control and supervision equipment in the prosumer's plant, embeds optimization models, can communicate reference values for active and reactive power and perform intentional islanding and demand response in portions of the distribution grid and microgrids. Two possible configurations have been tested: Single PoC (Point of Connection) and Multi-PoC. The results of the optimization model and in-field experiments are presented and discussed for the considered pilot site: the Savona Campus Smart Polygeneration Microgrid (University of Genova, Italy).

Study on ecological environment impact and soil and water conservation technology of power transmission and transformation projects in typical ecologically fragile areas in western China

In the steady development of social and economic, the field of power transmission and transformation engineering construction in our country gradually forms a new electric power development pattern, providing a solid foundation for the innovative development of electric power enterprises in a new era. Since power transmission and transformation projects will cause serious soil and water loss, which will have a direct impact on the stability of ecological environment in various regions, which is not conducive to the construction and operation of power transmission and transformation projects and the long-term development of social economy, how to construct a safe and standardized construction environment, scientifically deal with the ecological environment problems of power transmission and transformation projects, and rationally use soil and water conservation technology? It is the main problem that Chinese scholars study and discuss at present. On the basis of understanding the environmental status of typical ecologically fragile areas in western China, and according to the development of soil and water conservation technology in power transmission and transformation projects, this paper mainly studies the online monitoring system and application technology of soil and water conservation for power transmission and transformation projects in a certain area, so as to provide effective basis for the construction management of power transmission and transformation projects in the new era.

An Integration Framework of Secure Multiparty Computation and Deep Neural Network for Improving Drug-Drug Interaction Predictions.

Drug-drug interaction (DDI) is a key concern in drug development and pharmacovigilance. It is important to improve DDI predictions by integrating multisource data from various pharmaceutical companies. Unfortunately, the data privacy and financial interest issues seriously influence the interinstitutional collaborations for DDI predictions. We propose multiparty computation DDI (MPCDDI), a secure MPC-based deep learning framework for DDI predictions. MPCDDI leverages the secret sharing technologies to incorporate the drug-related feature data from multiple institutions and develops a deep learning model for DDI predictions. In MPCDDI, all data transmission and deep learning operations are integrated into secure MPC frameworks to enable high-quality collaboration among pharmaceutical institutions without divulging private drug-related information. The results suggest that MPCDDI is superior to other eight baselines and achieves the similar performance to that of the corresponding plaintext collaborations. More interestingly, MPCDDI significantly outperforms methods that use private data from the single institution. In summary, MPCDDI is an effective framework for promoting collaborative and privacy-preserving drug discovery.

Actions to Handle Increasing Distributed Generation and Rising Voltage during Low-Demand Periods: Tap Staggering Practices and Field Tests in the Italian Transmission Network

This paper deals with the voltage regulation strategies implemented in the Italian transmission network to face the increasing problem of high voltages during time periods characterized by low demand. After an introduction in which this very actual problem is discussed, the focus is on tap staggering practices. Although tap staggering is not a new idea, it is gaining practical importance only in the very last few years as a means of enhancing the inductive power drawn from the grid and, therefore, limiting the voltage rise. Accordingly, tap staggering contributes to the mitigation of the problems caused by the increasing penetration of renewable energy sources and thus can allow an increase in the share of renewable energy sources. The paper presents the different tap staggering practices that are being defined by the Italian transmission system operator and reports some tap staggering tests recently performed on large autotransformers as well as phase-shifter transformers.

Applying Fuzzy Time Series for Developing Forecasting Electricity Demand Models

Managing the energy produced to support industries and various human activities is highly relevant nowadays. Companies in the electricity markets of each country analyze the generation, transmission, and distribution of energy to meet the energy needs of various sectors and industries. Electrical markets emerge to economically analyze everything related to energy generation, transmission, and distribution. The demand for electric energy is crucial in determining the amount of energy needed to meet the requirements of an individual or a group of consumers. But energy consumption often exhibits random behavior, making it challenging to develop accurate prediction models. The analysis and understanding of energy consumption are essential for energy generation. Developing models to forecast energy demand is necessary for improving generation and consumption management. Given the energy variable’s stochastic nature, this work’s main objective is to explore different configurations and parameters using specialized libraries in Python and Google Collaboratory. The aim is to develop a model for forecasting electric power demand using fuzzy logic. This study compares the proposed solution with previously developed machine learning systems to create a highly accurate forecast model for demand values. The data used in this work was collected by the European Network of Transmission System Operators of Electricity (ENTSO-E) from 2015 to 2019. As a significant outcome, this research presents a model surpassing previous solutions’ predictive performance. Using Mean Absolute Percentage Error (MAPE), the results demonstrate the significance of set weighting for achieving excellent performance in fuzzy models. This is because having more relevant fuzzy sets allows for inference rules and, subsequently, more accurate demand forecasts. The results also allow applying the solution model to other forecast scenarios with similar contexts.

A distribution network planning method based on the integration of operation and planning and coordinated with the transmission network

With the increasing integration of renewable energy into the power grid, the traditional roles of the transmission and distribution networks have become less distinct at the operational level. The integration between distribution network planning (DNP) and the transmission and distribution networks operation is crucial to ensure grid stability. Existing research has primarily focused on collaborative operation control between transmission and distribution networks, leaving a gap in integrated DNP, since few works can handle the integer variables. This study proposes a distribution network planning method based on the integration of operation and planning and coordinated with the transmission network. It aims to minimize investment and operational costs while considering local generation units, distributed renewables, and network constraints. Using a heterogeneous decomposition algorithm (HGD), the optimization model alternates between the two networks, assisted by injected parameters for global optimality. A convolutional neural network (CNN) surrogate model is then used to rapidly optimize precise distribution network plans that coordinate with the transmission network. Experimental results on IEEE 30 and IEEE 69 cases demonstrate that the proposed approach offers valuable engineering benefits, reducing iteration counts by up to 20% and improving accuracy compared to other distributed algorithms.

Energy Market Transition and Climate Change: A Review of TSOs-DSOs C+++ Framework from 1800 to Present

In response to the pressing global challenges around climate change and the imperative of transitioning the energy market towards sustainability, this paper presents a comprehensive review starting from the late 18th century. The study examines the pivotal role of Transmission System Operators (TSOs) and Distribution System Operators (DSOs) in shaping the evolving energy landscape, with a specific emphasis on the C+++ Framework. This framework emphasizes coordination, cooperation, and collaboration between TSOs and DSOs to achieve sustainable energy systems through the integration of renewable energy technologies, storage systems, and efficient energy demand management. In addition, the review provides a historical overview of global warming from 1800 to the present, highlighting key events and developments related to greenhouse gas emissions. Furthermore, the paper delves into the significance of international agreements such as the Paris Agreement and the importance of reducing greenhouse gas emissions for a sustainable future. Recognizing the vital role of the C+++ Framework, the paper concludes with a discussion of future hybrid sustainable technologies incorporating various storage and efficient lighting technologies that can optimize energy management and reduce carbon emissions. This research aims to contribute valuable insights to inform energy policy and decision-making processes for a reliable, efficient, and sustainable energy delivery system.

Transmission and distribution network-constrained large-scale demand response based on locational customer directrix load for accommodating renewable energy

Large-scale demand response (DR) is a promising solution to mitigate the problem of renewable energy (RE) curtailment with the rising proliferation of RE in both the transmission system (TS) and distribution system (DS). However, the current DR schemes face significant limitations when applied at a large scale, and the lack of consideration of TS and DS networks stymies the potential of large-scale DR in facilitating RE accommodation. To address these gaps, this paper proposes a novel hierarchical DR scheme based on locational customer directrix load (LCDL) that takes into account both TS and DS, involving interactions among tri-layer entities: the transmission system operator, distribution system operators, and customers. Firstly, the concept of LCDLs is proposed to characterize the desired load profiles at various locations, considering the constraints of both TS and DS networks. Subsequently, transmission-level and distribution-level LCDLs are formulated respectively and leveraged to induce the load reshaping of flexible resources at pertinent locations, thereby unlocking the deliverable flexibilities over TS and DS. Furthermore, the collaborative interaction among the three layers is depicted by a two-loop Stackelberg game, and a distributed algorithm is presented to achieve an equilibrium solution without compromising the privacy of the respective entities. Case studies testify that the proposed DR scheme significantly enhances the capacity to accommodate RE in both TS and DS, maintains economic balance in the trading process of DR services, and benefits all involved entities. Conducted on a practical city power grid with a substantial share of RE and DR customers, the simulation test validates the scalability of the proposed DR scheme and results show that with a 10% increment of DR customers in one DS, the RE curtailment rate could be reduced by 3% and 15% for TS and DS, respectively.

Prioritization of transmission and distribution system operator collaboration for improved flexibility provision in energy markets

The effective use of the flexibility sources of transmission and distribution networks necessitates smooth coordination between transmission and distribution system operators (TSO–DSO) as well as proper interfacing between the energy and flexibility markets. In this paper, mathematical centralized/decentralized optimization frameworks of flexibility market structures is proposed for four market mechanisms of a transmission-level centralized market, a local distribution- and centralized transmission-level market, a TSO priority market, and a TSO–DSO price equilibrium market. Further, prioritization mechanisms are developed by allocating ramp flexibilities in multi-interval day-ahead market clearing procedures for the wholesale (transmission level) and local markets (distribution level) to reduce the prediction error and improve the performance of the real-time flexibility market, which is a single-interval optimization market. Accordingly, the concurrent provision of the energy and flexibility requirements of the transmission and distribution networks is explored in a joint energy and flexibility day-ahead market. As a base case, the centralized market model does not prioritize TSO–DSO flexibility. The second market model gives the DSO priority in terms of providing flexibility. The DSO starts by using the resources of the distribution network. It then makes the upstream market an offer for the flexibility capacities of the remaining unused resources. In the third proposed market, the TSO receives priority in providing flexibility. The optimization frameworks proposed for the third market mechanisms are formulated as mixed-integer linear programming models (MILP). In addition, to ensure a higher resilience, a lower complexity of algorithms, and the possibility to better adapt to the efficient flexibility allocation in the TSO/DSO collaboration, an approach of the decentralized alternating direction method of multipliers (ADMM) in the decentralized optimization framework is performed to clear the proposed wholesale market while taking into account the local markets at the distribution level. Finally, to evaluate the proposed multi-level centralized/decentralized optimization frameworks, simulation results are performed on a modified IEEE 30-bus/118-bus transmission network with three/five IEEE 33-node distribution networks connected. In the case studies, various market clearing and prioritization mechanisms are implemented and evaluated through numerical simulations.

Clustering Combined Multi-Objective Optimal Operation of Transmission Systems Considering Voltage Stability

In recent years, power systems have undergone major changes called energy transitions during which synchronous generators have been replaced with power electronics-based generation. Therefore, the voltage stability of power systems has become a major concern owing to the absence of synchronous generators. This study proposes multi-objective optimization using the non-dominated sorting genetic algorithm III to achieve optimal reactive power reserve procurement and improve the voltage stability of the overall system. These systematic approaches require high computational power and are unsuitable for the operational frameworks currently used for large-scale power systems. Previous works have rarely considered the local characteristics of reactive power or generation de-commitment with sufficient re-dispatch owing to greater renewable energy integration. We propose a framework for achieving systematic optimization by considering various objective functions while utilizing the regional aspect of reactive power via spectral clustering-based voltage control area (VCA) identification. The proposed method comprises systematic and regional approaches to optimizing systems for voltage stability improvement based on VCAs. The results demonstrate that the proposed method shows satisfactory performance. These results will be helpful for decision making for power system operations in harsher environments with more renewable energy.

Control of an Offshore Wind Farm Considering Grid-Connected and Stand-Alone Operation of a High-Voltage Direct Current Transmission System Based on Multilevel Modular Converters

This work presents a control strategy for integrating an offshore wind farm into the onshore electrical grid using a high-voltage dc transmission system based on modular multilevel converters. The proposed algorithm allows the high-voltage DC system to operate in grid-connected or stand-alone modes, with the second case supplying power to local loads. In either mode, the modular multilevel rectifier works as a grid-forming converter, providing the reference voltage to the collector network. During grid-connected operation, the modular multilevel inverter regulates the DC link voltage while the generating units are controlled to maximize power extracted from the wind turbines. Conversely, in the event of grid disconnection, the onshore modular multilevel converter takes over the regulation of the AC voltage at the point of connection to the grid, ensuring energy supply to local loads. Simultaneously, the generator controller transitions from tracking the maximum power of the wind turbines to regulating the DC link voltage, preventing excessive power injection into the transmission DC link. Additionally, the turbine pitch angle control regulates the speed of the generator. Mathematical models in the synchronous reference frame were developed for each operation mode and used to design the converter’s controllers. A digital model of the wind power plant and a high-voltage dc transmission system was implemented and simulated in the PSCAD/EMTDC program. The system modeled includes two groups of wind turbines, generators, and back-to-back converters, in addition to a DC link with a rectifier and an inverter station, both based on modular multilevel converters with 18 submodules per arm, and a 320 kV/50 km DC cable. Aggregate models were used to represent the two groups of wind turbines, where 30 and 15 smaller units operate in parallel, respectively. The performance of the proposed control strategy and the designed controllers was tested under three distinct scenarios: disconnection of the onshore converter from the AC grid, partial loss of a wind generator set, and reconnection of the onshore converter to the AC grid.

Particle swarm optimization based artificial neural network (PSO-ANN) model for effective k-barrier count intrusion detection system in WSN

Wireless Sensor Networks (WSN) offers an extensive array of possibilities and finds application to nearly every wake of human lifetime. Two of the methods are intrusion detection and monitoring in dangerous environments. Generally, WSN are extremely vulnerable to various kinds of network attacks, lifespan of whole network is reduced by interfering with data transmission and communication operations. A novel approach, provides accurate measurement of several obstacles to achieve rapid identification and avoidance of disruptions. In order to more accurately estimate k-barrier number for efficient intrusion detection and prevention, a Particle Swarm Optimization based Artificial Neural Network (PSO-ANN) is developed. The region's size, sensor's detecting field, area where sensor transmits its signals, several sensors constitute the four important features utilized for learning and evaluating PSO-ANN model. The extraction of the different attributes is achieved with the help of simulation through Monte Carlo. It has been inferred from the research that the approach is accurate in predicting number of invasions, correlation coefficient, root mean square error, accuracy, precision are achieved to be excellent in contrast to the remaining approaches.

Learning Data-Driven Stable Corrections of Dynamical Systems—Application to the Simulation of the Top-Oil Temperature Evolution of a Power Transformer

Many engineering systems can be described by using differential models whose solutions, generally obtained after discretization, can exhibit a noticeable deviation with respect to the response of the physical systems that those models are expected to represent. In those circumstances, one possibility consists of enriching the model in order to reproduce the physical system behavior. The present paper considers a dynamical system and proposes enriching the model solution by learning the dynamical model of the gap between the system response and the model-based prediction while ensuring that the time integration of the learned model remains stable. The proposed methodology was applied in the simulation of the top-oil temperature evolution of a power transformer, for which experimental data provided by the RTE, the French electricity transmission system operator, were used to construct the model enrichment with the hybrid rationale, ensuring more accurate predictions.

Joint Control Strategy of Energy Storage System and Cutting Machine for Transient Stability of Direct Current Locking Rear Delivery Terminal System

With the gradual operation of large-capacity HVDC transmission, HVDC), the characteristics of the “strong and weak communication” of the power grid are increasingly obvious. The power impact of the DC line after locking has a great impact on the power angle stability of the system and seriously threatens the transient stability of the delivery end system. By establishing the equivalent model of the AC/DC system with the energy storage power station and analyzing the transient process after DC locking, we propose a control strategy for the transient stability of the energy storage system and the delivery system after DC locking. This strategy controls the charge and discharge of the energy storage system by collecting real-time power angle and voltage data of the grid, uses the equal area rule, and initiates the cutting machine after the energy storage system is withdrawn. To verify the validity and correctness of the proposed method, a simulation analysis was performed in a modified cross-flow CEPRI-36 node AC–DC system.

Frequency regulated transmission-reflection integration modes of a terahertz metasurface.

The conventional transmission and reflection operating mode switching metasurface depends on phase change materials, which are often difficult to integrate with metasurface devices and work in real time. Here, we propose an integration of a transmission-reflection metasurface that can dynamically control beam direction and functions in both transmission and reflection modes by varying the frequency of the incident wave. Remarkably, the transmission and reflection modes of terahertz beam manipulation can be obtained by illuminating only the transmission side of the metasurface. The full-wave simulation results are in good agreement with the theoretically calculated results, which verifies the terahertz wave manipulation capability of the proposed structure. This metasurface provides a design method for full-space terahertz beam regulation devices.

A Hierarchical Maintenance Method for Power System Transmission and Substation Multi-Equipment Operation Status Considering Equipment Service Age

The current conventional maintenance methods for transmission and substation operation equipment are mainly used to extract fault data features by combining micro-network algorithms to process equipment operation data. The lack of effective analysis of equipment fault characteristics leads to poor maintenance results. In this regard, a power system transmission and substation multi-device operation status hierarchical maintenance method considering the age of equipment service is proposed. By accessing the configuration network and interacting with the grid intelligent collection of data, network storage and parallel processing of data are realized. The characteristics of transmission and substation equipment operation data are analyzed and processed to build the maintenance system. In the experiments, the proposed method is validated for the effect of condition assessment. The experimental results show that when the proposed method is used to process the transmission and substation equipment, the condition assessment accuracy is high and it has a more desirable equipment maintenance performance.

Optimisation of generation unit commitment and network topology with the dynamic thermal rating system considering N-1 reliability

The electrical grid is a vital component of modern society that requires significant and cost-effective investments to ensure its reliability. The dynamic thermal rating system (DTR) and network topology optimization (NTO) aim to increase the capacity of existing networks and provide flexible options for enhancing power system reliability. The co-optimization framework for generation unit commitment and network switching is formulated with for N-1 reliability, a gap that has not been previously addressed. This is important as it ensures: (1) economic efficiency in power system despatch, (2) optimal operation of both generation and transmission systems, and (3) power grid operation that can withstand the loss of any single component without causing a cascading failure. The reliability impacts of these new proposed operating strategies are also tested on complex, large-scale power systems under long-term, multi-area weather conditions. This proposed work is useful for power system planners, as it helps them identify ways to improve the reliability and efficiency of power systems under various weather conditions. This paper shows that using the DTR-NTO combination improves EENS by 50% in both test cases compared to the original condition. Additionally, it also makes the reliability assessment more realistic when taking into account multi-area weather conditions.