Backup protection requirements in future low-inertia power systems

The content of this paper will illustrate how, in the future, the power transmission system in Great Britain (GB) may be much "weaker" than it is at present, and will describe the potential impact that this could have on the voltage profiles during faults, and how the operation of backup protection system operation, if required, needs to be considered carefully to ensure system integrity in the future. The potential for future problems associated with generators’, converter-interfaced infeeds’, and HVDC interconnectors’ potential inability to “ride through” during slow/backup protection operations, and the consequent risk of complete system collapse, will also be highlighted. The paper also contains a description of ongoing and future work concerned with investigation of the use of wide-area communications systems, which may already be in existence and used for other purposes, to enhance backup protection performance and possibly offer an alternative and improved solution compared with existing schemes. It is shown how such a system could potentially be "settings-free" and establish and maintain an image of the connectivity of the network from either SCADA data and/or analysing current flows during normal operation. Example results of simulations are included to demonstrate the concept of identifying fault locations and protection failures using measured voltages from phasor measurement units (PMUs). This may act as a foundation for a future backup protection scheme and this is discussed in the conclusions and future work sections

Voltage Stability Assessment and Control Based on Synchrophasor Measurements in Ambient Condition

Voltage stability assessment and control are among the top priority tasks for electric utilities and power system operators. Failure in performing these correctly, has resulted in some of the major blackouts over the past few decades. Continuous loading increase on system infrastructure, higher reliability and security requirements, as well as progressive renewable resource integration, incapable of guaranteeing sustained reactive support, have all made this function more complex, and yet more crucial.Traditionally, developed algorithms for calculating voltage stability margin and recommending the necessary control action, rely on complete and accurate system models, as well as extensive offline studies. Investigating previous incidents has proved this to be a challenging goal to achieve. At the same time, integration of high-sampling measurement devices such as synchrophasors, powerful processors, and fast communication have resulted in increased situational awareness and monitoring quality of the system behavior. As a result, there is high interest today, to develop measurement-based real-time tools for determining system status from stability point of view and deduce the optimal control actions in critical conditions.In this dissertation, accurate modeling of the power system ambient data, corresponding to its operation at quasi steady-state condition is discussed, and the difference between system's steady-state response and power flow solutions is investigated. Based on this clarification, a new distributed model-free algorithm is proposed for voltage stability assessment and control of a power system, based on steady-state Q-V sensitivity of system buses. This is realized through subspace system identification from input-output response based on ambient measurements. Once the sensitivities are known, system proximity to voltage instability can be determined using the minimum sensitivity, rank system buses according to their stability margin and the need for corrective control actions, and determine the optimal set of actions needed to bring system operation point back to a secure state. To pursue the latter, two optimization approaches with different objectives based on various operating policies are introduced towards finding the best local control action within a substation. Finally, an index is introduced to determine vulnerability of the system to voltage instability caused by limitation on a generator's reactive power

Online monitoring and control of voltage stability margin via machine learning-based adaptive approaches

Voltage instability or voltage collapse, observed in many blackout events, poses a significant threat to power system reliability. To prevent voltage collapse, the countermeasures suggested by the post analyses of the blackouts usually include the adoption of better online voltage stability monitoring and control tools. Recently, the variability and uncertainty imposed by the increasing penetration of renewable energy further magnifies this need. This work investigates the methodologies for online voltage stability margin (VSM) monitoring and control in the new era of smart grid and big data. It unleashes the value of online measurements and leverages the fruitful results in machine learning and demand response. An online VSM monitoring approach based on local regression and adaptive database is proposed. Considering the increasing variability and uncertainty of power system operation, this approach utilizes the locality of underlying pattern between VSM and reactive power reserve (RPR), and can adapt to the changing condition of system. LASSO (Least Absolute Shrinkage and Selection Operator) is tailored to solve the local regression problem so as to mitigate the curse of dimensionality for large-scale system. Along with the VSM prediction, its prediction interval is also estimated simultaneously in a simple but effective way, and utilized as an evidence to trigger the database updating. IEEE 30-bus system and a 60,000-bus large system are used to test and demonstrate the proposed approach. The results show that the proposed approach can be successfully employed in online voltage stability monitoring for real size systems, and the adaptivity of model and data endows the proposed approach with the advantage in the circumstances where large and unforeseen changes of system condition are inevitable. In case degenerative system conditions are identified, a control strategy is needed to steer the system back to security. A model predictive control (MPC) based framework is proposed to maintain VSM in near-real-time while minimizing the control cost. VSM is locally modeled as a linear function of RPRs based on the VSM monitoring tool, which convexifies the intricate VSM-constrained optimization problem. Thermostatically controlled loads (TCLs) are utilized through a demand response (DR) aggregator as the efficient measure to enhance voltage stability. For such an advanced application of the energy management system (EMS), plug-and-play is a necessary feature that makes the new controller really applicable in a cooperative operating environment. In this work, the cooperation is realized by a predictive interface strategy, which predicts the behaviors of relevant controllers using the simple models declared and updated by those controllers. In particular, the customer dissatisfaction, defined as the cumulative discomfort caused by DR, is explicitly constrained in respect of customers\u27 interests. This constraint maintains the applicability of the control. IEEE 30-bus system is used to demonstrate the proposed control strategy. Adaptivity and proactivity lie at the heart of the proposed approach. By making full use of real-time information, the proposed approach is competent at the task of VSM monitoring and control in a non-stationary and uncertain operating environment