• Energy Research

AbstractElectrical power systems are continuously upgrading into networks with a higher degree of automation capable of identifying and reacting to different events that may trigger undesirable situations. In power systems with decreasing inertia and damping levels, poorly damped oscillations with sustained or growing amplitudes following a disturbance may eventually lead to instability and provoke a major event such as a blackout. Additionally, with the increasing and considerable share of renewable power generation, unprecedented operational challenges shall be considered when proposing protection schemes against unstable electro‐mechanical (e.g. ringdown) oscillations. In an emergency situation, islanding operations enable splitting a power network into separate smaller networks to prevent a total blackout. Due to such changes, identifying the underlying types of oscillatory coherency and the islanding protocols are necessary for a continuously updating process to be incorporated into the existing power system monitoring and control tasks. This paper examines the existing evaluation methods and the islanding protocols as well as proposes an updated operational guideline based on the latest data‐analytic technologies.

This paper proposes a probabilistic small signal stability assessment (PSSSA) methodology based on the application of Monte Carlo approach for iterative evaluation, via modal analysis of small signal stability (SSS). Operation states represented by random values of generation and demand are analyzed. A probabilistic instability risk index based on cumulative probability distribution function of damping ratios of oscillatory modes is calculated, as well as a power system stabilizer (PSS) devices location index based on eigenvectors and participation factors, which are considered random variables. Moreover, the impact of long-distance power flows on oscillatory modes (OM) and how the damping of OM depends on the orientation and magnitude of power flows is investigated. Further, an additional index concerns qualitatively the determination of transfer capability as affected by small signal stability. PSSSA is tested on a reduced order model of New England-New York's interconnected system considering uncertainties around three different system conditions separately: highly loaded, fairly loaded, and lowly loaded. The results highlight the main advantages of PSSSA over deterministic SSS studies such as instability risk assessment, small signal stability enhancement through adequate PSS location, and the proposal of possible restrictions for transfer capability in order to avoid poorly damped oscillations in the face of the diversity in power system operation.

For the future, a large‐scale expansion of offshore wind energy is expected in Europe. To collect this wind energy and to enable electricity trading between countries, an offshore network will be implemented in the North Sea. Maintaining a high level of security of supply at affordable costs is one of the key objectives in the design and operation of power systems and therefore, the reliability of offshore grids is an important topic of discussion. Whereas onshore, the security of supply is assured by reliability criteria like n‐1 redundancy, the same n‐1 redundancy might not be an economical solution for offshore networks. For todays (small) offshore networks, n‐1 redundancy is hardly economically justifiable, seen from a wind farm owner's point of view. The question then arises how the reliability of large‐scale offshore networks should be evaluated and what measures can be taken to maintain a high security of supply onshore. This paper aims at discussing this topic by reviewing the results of recent research work. It is found that whereas for smaller offshore networks reliability evaluation is mainly an economic analysis seen from a wind farm owner's point of view, for large‐scale offshore networks, it is necessary to consider the interaction of offshore–onshore networks in reliability analysis. It is proposed to analyze the reliability of combined offshore–onshore power systems in an integrated approach, such that various (offshore and onshore) measures can be considered to find the most economical solution.This article is categorized under: Wind Power > Systems and Infrastructure Energy Infrastructure > Systems and Infrastructure Energy Systems Economics > Systems and Infrastructure

Abstract The net-zero energy buildings are often supplied by renewable resources and energy storage systems. These energy resources have different seasonal and daily patterns of power production. Their output power is also uncertain. This paper aims to study these issues including daily-seasonal operation patterns, uncertainty, and cogeneration of various renewable resources and storage systems. These issues are investigated at net-zero energy building supported by renewable resources (i.e., solar energy, hydro energy, and fuelcell) and energy storage systems (i.e., hydrogen storage system). The uncertain parameters of the model are solar-hydro-load powers. The model minimizes the investment cost on solar system. The plan finds optimal sizing and operation for solar, hydro, hydrogen, and fuel-cell. The cooperation of hydrogen storage and fuelcell is optimized to level the uncertainty. The surplus of energy is fed into water electrolyzer to produce hydrogen and the fuelcell consumes the hydrogen to produce electricity. The seasonal operation is dealt by cogeneration of hydro-solar systems. The proposed plan installs 73 kW solar panel. The hydrogen storage system is charged at hours 7–17. When hydro power is increased to 39 kW, the building does not need the solar energy. The proposed model decreases the Carbon Dioxide by about 39546 kg. The model also reduces the total cost by about 50.3%.

Several smart grid applications have recently been devised in order to timely perform supervisory functions along with self‐healing and adaptive countermeasures based on system‐wide analysis, with the ultimate goal of reducing the risks associated with potentially insecure operating conditions. Real‐time transient stability assessment (TSA) belongs to this type of applications, which allows deciding and coordinating pertinent corrective control actions depending on the evolution of post‐fault rotor‐angle deviations. This study presents a novel approach for carrying out real‐time TSA based on prediction of area‐based centre‐of‐inertia (COI) referred rotor angles from phasor measurement unit (PMU) measurements. Monte Carlo‐based procedures are performed to iteratively evaluate the system transient stability response, considering the operational statistics related to loading condition changes and fault occurrence rates, in order to build a knowledge database for PMU and COI‐referred rotor‐angles as well as to screen those relevant PMU signals that allows ensuring high observability of slow and fast dynamic phenomena. The database is employed for structuring and training an intelligent COI‐referred rotor‐angle regressor based on support vector machines [support vector regressor (SVR)] to be used for real‐time TSA from selected PMUs. Besides, the SVR is optimally tuned by using the swarm variant of the mean‐variance mapping optimisation. The proposal is tested on the IEEE New England 39‐bus system. Results demonstrate the feasibility of the methodology in estimating the COI‐referred rotor angles, which enables alerting about real‐time transient stability threats per system areas, for which a transient stability index is also computed.

As the integration of renewable energy accelerates, ensuring power system stability becomes increasingly critical. This research utilized a Root Mean Square (RMS) synthetic model of the future 380 kV Dutch power system towards 2050 to analyze its oscillatory stability under high renewable penetration and the impact of grid-forming converters under various parametrizations. The presented case study shows that grid-forming (GFM) converters significantly improve frequency stability and damping performance across different perturbations, particularly at higher GFM penetration levels, improving frequency and damping parameters. However, various oscillatory modes present potential stability risks at high penetration levels. The case study also shows minimal differences in controller selection in large-scale models, except under certain conditions. Additionally, the analysis of controller parameters highlighted the critical importance of tuning active power parameters to ensure system stability. The investigation provides essential insights for future power systems, where large-scale integration of renewable energy will necessitate the implementation of converters able to provide ancillary services. The findings emphasize the importance of optimizing GFM converter settings and penetration levels to maintain system resilience, offering valuable guidance for future system planning and regulatory frameworks.

Operational planning of power systems, especially in terms of overall reliability and security, is a key issue in the smart grid development. Hence, it is necessary to develop new strategies to cope with increasing uncertainties arising from the fast changing ways power systems are being operated. This paper presents a comprehensive approach to determine an optimal transmission network expansion plan considering the enhancement of small-signal stability through wide-scale deployment of the existing and planned transmission system assets. The dynamic model of the transmission network operational planning (TNOP) is solved based on a combination of the Mean-Variance Mapping Optimization (MVMO), and the classic dynamic programming method embedded with a heuristic procedure. Besides, a probabilistic eigenanalysis-based recursive method is proposed to determine the optimal control strategies that are highly relevant to the enhancement of the system small-signal stability performance throughout the planning horizon. Numerical results demonstrate the viewpoint and the effectiveness of the proposed approach in providing optimal strategies of minimum cost while avoiding the instability risk associated to poorly damped low-frequency electromechanical oscillations.

This paper aims at assessing the power system reliability by estimating loss of load (LOL) index using mutual information based Bayesian approach. Reliability analysis is a key component in the design, analysis and tuning of complex structure like electrical power system. Consideration is given to rare events while constructing the Bayesian network, which provides reliable estimates of probability distribution function of LOL with lesser computing effort. Also, the ranking of load components due to loss of load is evaluated. The RBTS and IEEE RTS-24 systems are used as test cases. 6 pages, 9 figures, 2 tables, PowerTech, 2015 IEEE Eindhoven

Large amounts of offshore wind energy are planned by the Dutch government for the next years. The power system will continue to evolve to integrate these new sources of green power efficiently. The dynamic system performance of a grid topology with transmission capacity above 1 GW connecting offshore wind farms (OWFs) at distances close to 100 km in a standardized and modular manner is the focus of this paper. An electromagnetic transient (EMT) model was built in PSCAD in which OWFs are connected with 66 kV cables to a centralized offshore platform and a transmission link with a capacity of 1050 MW. The connection to the onshore transmission network is through a high voltage direct current (HVDC) link that uses a modular multilevel converter (MMC) topology. A dynamic performance analysis with several expected operational cases is done to obtain a complete overview and understanding of the system.