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Modern electrical power systems are characterized by a high rate of transient processes, the use of digital monitoring and control systems, and the accumulation of a large amount of technological information. The active integration of renewable energy sources contributes to reducing the inertia of power systems and changing the nature of transient processes. As a result, the effectiveness of emergency control systems decreases. Traditional emergency control systems operate based on the numerical analysis of power system dynamic models. This allows for finding the optimal set of preventive commands (solutions) in the form of disconnections of generating units, consumers, transmission lines, and other primary grid equipment. Thus, the steady-state or transient stability of a power system is provided. After the active integration of renewable sources into power systems, traditional emergency control algorithms became ineffective due to the time delay in finding the optimal set of control actions. Currently, machine learning algorithms are being developed that provide high performance and adaptability. This paper contains a meta-analysis of modern emergency control algorithms for power systems based on machine learning and synchronized phasor measurement data. It describes algorithms for determining disturbances in the power system, selecting control actions to maintain transient and steady-state stability, stability in voltage level, and limiting frequency. This study examines 53 studies piled on the development of a methodology for analyzing the stability of power systems based on ML algorithms. The analysis of the research is carried out in terms of accuracy, computational latency, and data used in training and testing. The most frequently used textual mathematical models of power systems are determined, and the most suitable ML algorithms for use in the operational control circuit of power systems in real time are determined. This paper also provides an analysis of the advantages and disadvantages of existing algorithms, as well as identifies areas for further research. [ABSTRACT FROM AUTHOR]

This paper examines the use of manifold learning in the context of electric power system transient stability analysis. Since wide-area monitoring systems (WAMSs) introduced a big data paradigm into the power system operation, manifold learning can be seen as a means of condensing these high-dimensional data into an appropriate low-dimensional representation (i.e., embedding) which preserves as much information as possible. In this paper, we consider several embedding methods (principal component analysis (PCA) and its variants, singular value decomposition, isomap and spectral embedding, locally linear embedding (LLE) and its variants, multidimensional scaling (MDS), and others) and apply them to the dataset derived from the IEEE New England 39-bus power system transient simulations. We found that PCA with a radial basis function kernel is well suited to this type of power system data (where features are instances of three-phase phasor values). We also found that the LLE (including its variants) did not produce a good embedding with this particular kind of data. Furthermore, we found that a support vector machine, trained on top of the embedding produced by several different methods was able to detect power system disturbances from WAMS data. [ABSTRACT FROM AUTHOR]

As shares of variable renewable energy (VRE) on the electric grid increase, sources of grid flexibility will become increasingly important for maintaining the reliability and affordability of electricity supply. Lithium-ion battery energy storage has been identified as an important and cost-effective source of flexibility, both by itself and when coupled with VRE technologies like solar photovoltaics (PV) and wind. In this study, we explored the current and future value of utility-scale hybrid energy systems comprising PV, wind, and lithium-ion battery technologies (PV-wind-battery systems). Using a price-taker model with simulated hourly energy and capacity prices, we simulated the revenue-maximizing dispatch of a range of PV-wind-battery configurations across Texas, from the present through 2050. Holding PV capacity and point-of-interconnection capacity constant, we modeled configurations with varying wind-to-PV capacity ratios and battery-to-PV capacity ratios. We found that coupling PV, wind, and battery technologies allows for more effective utilization of interconnection capacity by increasing capacity factors to 60%–80%+ and capacity credits to close to 100%, depending on battery capacity. We also compared the energy and capacity values of PV-wind and PV-wind-battery systems to the corresponding stability coefficient metric, which describes the location-and configuration-specific complementarity of PV and wind resources. Our results show that the stability coefficient effectively predicts the configuration-location combinations in which a smaller battery component can provide comparable economic performance in a PV-wind-battery system (compared to a PV-battery system). These PV-wind-battery hybrids can help integrate more VRE by providing smoother, more predictable generation and greater flexibility.

This work presents a new approach to establishing the minimum requirements for anti-islanding protection of distributed energy resources (DERs) with focus on bulk power system stability. The proposed approach aims to avoid cascade disconnection of DERs during major disturbances in the transmission network and to compromise as little as possible the detection of real islanding situations. The proposed approach concentrates on the rate-of-change of frequency (RoCoF) protection function and it is based on the assessment of dynamic security regions with the incorporation of a new and straightforward approach to represent the disconnection of DERs when analyzing the bulk power system stability. Initially, the impact of disconnection of DERs on the Brazilian Interconnected Power System (BIPS) stability is analyzed, highlighting the importance of modeling such disconnection in electromechanical stability studies, even considering low penetration levels of DERs. Then, the proposed approach is applied to the BIPS, evidencing its benefits when specifying the minimum requirements of anti-islanding protection, without overestimating them.

Determining security/stability boundaries is a common and critical means of preventing cascading failures induced by voltage-related issues, which represents one of the major challenges in bulk power systems. However, traditional approaches suffer from conservative issues and heavy computational burdens. To address these challenges, the concept of an autonomous-synergic voltage security region (AS-VSR) and the corresponding dynamic constraint coefficient pruning (Deep) computation method, which fully consider the volt/var characteristics of bulk power systems, are proposed in this letter. Both linearized and nonlinearized robust optimization problems are introduced to obtain accurate results. The computational accuracy, time cost, and advantages of autonomous-synergic control are observed in the simulation results.

This paper examines the use of manifold learning in the context of electric power system transient stability analysis. Since wide-area monitoring systems (WAMSs) introduced a big data paradigm into the power system operation, manifold learning can be seen as a means of condensing these high-dimensional data into an appropriate low-dimensional representation (i.e., embedding) which preserves as much information as possible. In this paper, we consider several embedding methods (principal component analysis (PCA) and its variants, singular value decomposition, isomap and spectral embedding, locally linear embedding (LLE) and its variants, multidimensional scaling (MDS), and others) and apply them to the dataset derived from the IEEE New England 39-bus power system transient simulations. We found that PCA with a radial basis function kernel is well suited to this type of power system data (where features are instances of three-phase phasor values). We also found that the LLE (including its variants) did not produce a good embedding with this particular kind of data. Furthermore, we found that a support vector machine, trained on top of the embedding produced by several different methods was able to detect power system disturbances from WAMS data.

As smart load adoption grows on the electric power system, potential for losing load diversity increases, possibly in ways that impact system stability. Cloud computing resources are able to coordinate large amounts of behind-the-meter loads and resources. Inadvertent or malicious actions could potentially result in gigawatts of load, distributed across large regions, acting nearly simultaneously. We study the resulting impacts of such a perturbation, which were previously recognized, with improved fidelity and granularity using a physically-based power system and demand model. The ResStock tool was used to calculate residential air conditioning load at more than 3,000 locations across the Western Interconnection, corresponding in time to heavy summer and light spring loading. Under an assumption that one cloud platform managed smart thermostats controlling 10%, 15%, or 20% of residential air-conditioning, calculated load steps could be injected into Positive Sequence Load Flow dynamic simulations. These load-driven effects were coupled with two classes of distributed generation ride-through to evaluate the potential for cascading outages. We found frequency deviations in the spring case far exceed the credible contingency event, leading to widespread distributed generation loss, while voltage depressions during the summer loading lead to widespread distributed generation loss and system separation.

With rapid growth of wind power in power systems, it becomes important to accurately model the behavior of wind, its interaction with conventional sources and also with other wind resources in order to conduct a realistic assessment of system reliability and benefits from wind energy utilization. At low wind penetration levels, all the wind energy generated is utilized to serve the load. However, at higher penetration levels, wind energy is spilled due to limitations in the ramping capability of the scheduled generating units and transfer capability of transmission lines. The benefits from wind energy are reduced as its spillage increases. Hence, accurate wind models should be developed to include forced wind energy curtailment in the reliability modelling, considering factors such as the system load level, unit dispatch order, ramp rates of the generating units and wind profile diversity between multiple wind farms. A new technique is proposed in this paper to create a comprehensive wind absorption capability model, and embed it in the composite generation and transmission system reliability model. The presented methodology to evaluate bulk system adequacy and wind energy benefits considering wind curtailment due to both the generation and transmission constraints is illustrated on an example system.

Proper long-term planning for investment in resources, timely operational planning to prepare resources and to decide on operational strategies, and proper operating decisions to respond to disturbances during real-time system operation are important to supply reliable power to customers as economically as possible. However, existing utility procedures are insufficient to comprehend uncertainties of modern renewable-integrated power systems and to provide suitable quatitative indicators to assist in operational planning. Independent system operators and utilities around the globe are developing new and unique approaches to operational planning to manage rising uncertainties in power generation from renewable sources like wind and PV. It is desirable to establish uniformity in operational adequacy evaluation methods and quantitative metrics applicable to all power systems in the operational planning horizon of days, weeks, or even months. This will help standardize the operational planning methodology and metrics, and simplify implementation of operational strategies. To address this need, this paper presents a probabilistic analytical methodology for operational adequacy evaluation of a bulk power system integrating the concepts of state enumeration and a novel Dynamic System State Probability Evaluation (DSSPE) approach in time series analysis to accommodate the operational as well as network characteristics. The proposed methodology is implemented on a test system to demonstrate operational adequacy-based operational planning, and to analyze the impact of factors such as unit commitment decisions, locational distribution of load, and generation on the operational adequacy of the system. [ABSTRACT FROM AUTHOR]

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With increased penetration of converter interfaced generation (CIG) in power systems, the suitability of modelling approaches and models dependent on the system conditions has to be studied. However, their suitability has yet to be systematically analysed. In this study, the authors first develop a methodology for identifying the maximum penetration of aggregated rooftop sources that can be represented as negative loads in both the steady state and dynamic analysis. Then they verify the suitability of a positive‐sequence performance‐based model and a detailed electromagnetic transient (EMT) model for utility‐scale photovoltaic (PV) plants under different fault conditions. Based on the results, they perform dynamic simulations on a large practical system with both positive‐sequence transient stability (TS) and EMT‐TS hybrid simulation. This study shows that positive‐sequence performance based PV plant model is more suitable for study cases with faults relatively far away from the point of connection. In addition, the results indicate that positive‐sequence models and TS simulation approach can produce comparable results with reference to the EMT‐TS hybrid simulation with EMT detailed models for normally‐cleared single‐line‐to‐ground faults, but the inadequacy of positive‐sequence based model and simulation approach becomes obvious for prolonged‐tripping of unbalanced faults in the proximity of the PV plants. [ABSTRACT FROM AUTHOR]

Several parallel restored subsystems exist in the last stage of the build-up restoration process of bulk power systems. The whole system restoration is completed when these subsystems are reconnected and their internal loads are recovered. This paper proposes a robust distributed coordination scheme to achieve reconnection and load recovery of parallel restored subsystems in wind power penetrated transmission systems. First, robust distributed restoration models are constructed considering individual subsystems under uncertain conditions. Then, an inner-outer nested upper-lower (IOUL) iterative algorithm is developed to solve the constructed models. The robust distributed coordination can be realized according to convergence. The proposed algorithm solves the complex non-convex model with uncertain variables by iteratively solving the small-scale mixed-integer linear programming (MILP) problem. The robust distributed restoration scheme allows independent decision-making within subsystems and ensures the feasibility of the distributed restoration strategy under uncertain conditions. The effectiveness of the proposed method is validated using three interconnected 6-bus systems and the IEEE 118-bus system, showing improved computational efficiency and restoration acceleration. [ABSTRACT FROM AUTHOR]

The widespread penetration of electric vehicles (EVs) in the automotive market will create not only concerns about additional energy burden on power grids but also incentives to further encourage vehicle to grid (V2G) functionalities. The integration of EVs with intermittent renewables requires complex control systems due to the need to predict and manage data related to user behaviour and climate. In this study, a real-time energy storage management system was developed to accommodate V2G technology integration in a residential nanogrid considering no prior knowledge about the future system's state. The rule-based algorithm developed maximises the direct use of solar energy, ensures a user-determined driving range and provides ancillary services to the utility grid in real-time through an autonomous distributed control. The considered residential nanogrid set up was modelled via Matlab/Simulink and the corresponding results are demonstrated and discussed. From simulations, it is shown that the smart house designed integrates the V2G technology so that the nanogrid provides ancillary services to the bulk power system causing the least possible energy burden on the utility. [ABSTRACT FROM AUTHOR]

The widespread penetration of electric vehicles (EVs) in the automotive market will create not only concerns about additional energy burden on power grids but also incentives to further encourage vehicle to grid (V2G) functionalities. The integration of EVs with intermittent renewables requires complex control systems due to the need to predict and manage data related to user behaviour and climate. In this study, a real-time energy storage management system was developed to accommodate V2G technology integration in a residential nanogrid considering no prior knowledge about the future system's state. The rule-based algorithm developed maximises the direct use of solar energy, ensures a user-determined driving range and provides ancillary services to the utility grid in real-time through an autonomous distributed control. The considered residential nanogrid set up was modelled via Matlab/Simulink and the corresponding results are demonstrated and discussed. From simulations, it is shown that the smart house designed integrates the V2G technology so that the nanogrid provides ancillary services to the bulk power system causing the least possible energy burden on the utility.

Islanded microgrids allow for a continuous supply of customers even when there is an outage in the bulk power system. The frequency control and stability in microgrids is an ongoing field of research. This work investigates the influence of synthetic inertia control of doubly-fed induction generator wind turbines on the dynamics of inverter dominated microgrids with droop control. A special focus is placed on the impact of the phase-locked loop dynamics.

This paper proposes a new decentralized restoration scheme in order to realize efficient coordinated restoration of the coupled transmission and distribution (TS-DS) system. First, considering the separated operation of the TS operator (TSO) and DS operators (DSOs) and to avoid the TSO data processing burden, the TS-DS system is divided into several subsystems according to their physical connection. Then, in order to implement the independent but coordinated decision-making of the subsystems, a decentralized decision-making framework is constructed by model decoupling and iterative interaction between TS and DS. Furthermore, for the sake of the convergence of the iterative process with non-convex models, a new decentralized optimization method is developed to obtain the optimal coordinated restoration strategy of the TS-DS system. The proposed decentralized restoration scheme achieves independent decision-making of the TSO and DSOs in the coupled TS-DS system, and solves the non-convergence problem of the decentralized optimization method. The effectiveness of the proposed method is validated using two IEEE standard test systems and a real-world power system. [ABSTRACT FROM AUTHOR]

In a bulk power system, generators can be divided into groups based on the variation of their swing curves after disturbance occurrence. In this regard, generators that have the same variations are placed in the same group and are called 'coherent'. Identifying coherent generators is an important step in creating a reduced model of bulk power systems for dynamic wide‐area analysis. The aim of this study is to provide a comprehensive and up‐to‐date bibliographic review of the literature related to coherency detection and power system partitioning after disturbance occurrence. In this study, coherency detection methods are classified into model‐based and measurement‐based categories. Furthermore, existing methods for power system partitioning based on coherency concept are also reviewed and categorised using two different criteria. Finally, the future trend toward coherency and coherent areas detection is discussed. [ABSTRACT FROM AUTHOR]

At the moment of writing, in Italy, there is an ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Its outbreak is leading to severe global socioeconomic disruptions impacting on all economic sectors from tourism, industry and the tertiary sector, up to the operational and opening of public offices, the closure of schools and the organization of families. Measures adopted by the Italian government to deal with the COVID-19 emergency have had direct effects both on people’s daily lives and on the activity of most industrial and commercial production companies. These changes have been unequivocally reflected also on the Italian electricity system, which has shown unprecedented behavior in terms of both energy consumption and volume—and subsequently, in the observed share of renewable and conventional production technologies. The goal of this study is to show the impact on the power industry of all the restrictions and lockdown of the activities in Italy and to discuss the effects of COVID-19 outbreak on the bulk power system and the entire electricity sector. In particular, the consequences on load profiles, electricity consumption and market prices in Italy, including the environmental aspects, are examined.

With rapid growth of wind power in power systems, it becomes important to accurately model the behavior of wind, its interaction with conventional sources and also with other wind resources in order to conduct a realistic assessment of system reliability and benefits from wind energy utilization. At low wind penetration levels, all the wind energy generated is utilized to serve the load. However, at higher penetration levels, wind energy is spilled due to limitations in the ramping capability of the scheduled generating units and transfer capability of transmission lines. The benefits from wind energy are reduced as its spillage increases. Hence, accurate wind models should be developed to include forced wind energy curtailment in the reliability modelling, considering factors such as the system load level, unit dispatch order, ramp rates of the generating units and wind profile diversity between multiple wind farms. A new technique is proposed in this paper to create a comprehensive wind absorption capability model, and embed it in the composite generation and transmission system reliability model. The presented methodology to evaluate bulk system adequacy and wind energy benefits considering wind curtailment due to both the generation and transmission constraints is illustrated on an example system. [ABSTRACT FROM AUTHOR]

Nonsynchronous generators such as photovoltaics (PVs) are expected to undermine bulk power systems (BPSs)’ frequency response at high penetration levels. Though the underlying mechanism has been relatively well understood, the accurate assessment and effective enhancement of the U.S. interconnections’ frequency response under extra-high PV penetration conditions remains an issue. In this paper, the industry-provided full-detail interconnection models were further validated by synchrophasor frequency measurements and realistically-projected PV geographic distribution information were used to develop extra-high PV penetration scenarios and dynamic models for the three main U.S. interconnections, including Eastern Interconnection (EI), Western Electricity Coordinating Council (WECC), and Electric Reliability Council of Texas (ERCOT). Up to 65% instantaneous PV and 15% wind penetration were simulated and the frequency response change trend of each U.S. interconnection due to the increasing PV penetration level were examined. Most importantly, the practical solutions to address the declining frequency response were discussed. This paper will provide valuable guidance for policy makers, utility operators and academic researchers not only in the U.S. but also other countries in the world. [ABSTRACT FROM PUBLISHER]

Active frequency response (AFR) control is needed in current power systems. To solve the over-frequency problems of generators connected to non-disturbed buses during the AFR control period, the generators should be clustered into coherent groups. Thus, the control efficiency can be improved on the premise of ensuring control accuracy. Since the influencing factors (such as the model parameters, operation modes, and disturbance locations, etc.) of power system operation can be comprehensively reflected by the generator frequency, which is easily collected and calculated, the generator frequency can be used as the coherency identification input. In this paper, we propose a coherency identification method of AFR control based on support vector clustering for a bulk power system. By mapping data samples from the initial space to the high-dimensional feature space, the radius of the minimal enclosing sphere that can envelop all the data samples is obtained. Moreover, the coherency identification of generators is determined for AFR control according to the evaluating method of AFR clustering control effects and the evaluating index of cluster compactness and separation. The simulation results for the modified New England IEEE 10-generator 39-bus system and Henan power grid show that the proposed method is feasible and effective.

This study proposes a novel method to partition volt/VAR control areas in bulk power systems using spectral clustering. By revealing the internal connectivity structure of the network considering the power‐flow‐based volt/VAR model, an undirected edge‐weighed graph is formed according to the defined electrical distance. On the basis of the graph theory, a novel approach is proposed through appropriate volt/VAR control area number selection, quality factor design, subspace projection, and spectral clustering. The approach is to partition the system into multiple volt/VAR control areas that have strong internal connections but weak external connections. Meanwhile, the proposed approach maintains the reactive power balances in each individual area and the minimum reactive power flow between those areas to facilitate area voltage regulations. Simulation results based on the New England 39 and IEEE‐118 test systems demonstrate the effectiveness of the proposed method. In addition, the Union for the Coordination of the Transmission of Electricity system is further employed to evaluate the performance in practical bulk systems. These results show that the proposed approach can partition volt/VAR control areas appropriately in the power system. [ABSTRACT FROM AUTHOR]

Stochastic subspace identification (SSI) methods have been widely employed for oscillatory mode identification on probing and ambient data and are reported to have good performances. This work proposes a novel SSI‐based approach for identifying dominant oscillatory mode from measurement data and extends the application of SSI to ringdown condition. The proposed approach first constructs an initial cluster of eigenvalues from SSI with repetitive calculations and then utilises a novel hierarchical clustering method to extract the dominant modes from the initial cluster. The repetitive calculations within the SSI are performed through varying the model order over a range defined by a novel initial order determination process. By doing so the challenge of model order determination for SSI‐based methods is resolved. Moreover, benefiting from the repetitive calculations and the clustering process, the proposed approach is highly immune to prevalent noises in the measurements. Finally, the proposed approach is applied and validated on the field‐measurement data from the phasor measurement units of China Southern Power Grid (CSG) through comparisons with Prony, ARMAX, and Monte Carlo methods. Test results demonstrate that the proposed approach performs with high accuracy, robustness, and efficiency in CSG. [ABSTRACT FROM AUTHOR]

The limited predictability and high variability of renewable generations has brought significant challenges on the real-time operation of bulk power systems. This paper proposes the concept of real-time dispatchability (RTDA) of power systems with variable energy resources, which focuses on investigating the impact of operating constraints and the cost of corrective actions on the flexibility of real-time dispatch. RTDA is the largest region in the uncertainty space, such that all the elements in it will not cause infeasibility while deploying a corrective action. This paper proposes a closed polyhedral form of RTDA. Moreover, an adaptive constraint generation algorithm is proposed to compute the boundaries of RTDA. Three potential applications are suggested. Case studies on the IEEE 118-bus system illustrate the RTDA concept and demonstrate the validity and efficiency of the proposed method in practical applications. [ABSTRACT FROM PUBLISHER]

Electric power industry is experiencing a movement from the existing conventional electric grid to a more reliable, efficient and secure smart grid. In order to achieve these goals, components such as energy storage will be included, and potentially in large scale. Many feasible applications of energy storage in power systems have been investigated. The major benefits of energy storage include electric energy time-shift, frequency regulation and transmission congestion relief. In this paper, we focus on the reliability improvement of the bulk power system brought by the utilization of energy storage in the local distribution systems integrated with renewable energy generation. An intelligent operation strategy for energy storage which improves reliability considering the renewable energy integration is presented. The smart grid communication and control network is utilized to implement the proposed energy storage operation. A bulk power system reliability evaluation framework is proposed to study the reliability impact brought by the energy storage integration and operation. A detailed case study and sensitivity analysis is performed to demonstrate the effectiveness of the presented operation strategy and evaluation framework, and to provide valuable insights on the power system reliability impact derived from the energy storage integration. [ABSTRACT FROM PUBLISHER]