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24 records · Page 2

Frequency Response Analysis Tool

Frequency response has received a lot of attention in recent years at the national level, which culminated in the development and approval of North American Electricity Reliability Corporation (NERC) BAL-003-1 Frequency Response and Frequency Bias Setting Reliability Standard. This report is prepared to describe the details of the work conducted by Pacific Northwest National Laboratory (PNNL) in collaboration with the Bonneville Power Administration and Western Electricity Coordinating Council (WECC) Joint Synchronized Information Subcommittee (JSIS) to develop a frequency response analysis tool (FRAT). The document provides the details on the methodology and main features of the FRAT. The tool manages the database of under-frequency events and calculates the frequency response baseline. Frequency response calculations are consistent with frequency response measure (FRM) in NERC BAL-003-1 for an interconnection and balancing authority. The FRAT can use both phasor measurement unit (PMU) data, where available, and supervisory control and data acquisition (SCADA) data. The tool is also capable of automatically generating NERC Frequency Response Survey (FRS) forms required by BAL-003-1 Standard.

97 MATHEMATICS AND COMPUTING↗

Loads as a Resource: Frequency Responsive Demand

Current power grid operation predominantly relies on scheduling and regulating generation resources to supply loads and balance load changes. Due to the inherent intermittency of renewable energy, more flexible and fast ramping capacity is required to compensate for the uncertainty and variability introduced by renewable energy resources. With the advancement of information technologies, power system end-use loads are becoming more agile and can participate in provision of balancing energy and other grid services. The use of demand response can greatly reduce the required generation reserve in a clean and environmentally friendly way. In this report, a new frequency responsive load (FRL) controller was proposed based on the GFA controller, which can respond to both over and under-frequency events. A supervisory control was introduced to coordinate the autonomous response from FRLs in order to overcome the issues of excessive system response due to high penetration of FRLs. The effectiveness of the proposed FRL controller was demonstrated by large-scale simulation studies on the WECC system. Specifically, the FRLs were deployed in the WECC system at different penetration levels to analyze the performance of the proposed strategy both with and without supervisory level control. While both methods have their own advantages, the case without supervisory control could lead to system-wide instability depending on the size of the contingency and the number of FRLs deployed in the system. In addition, the voltage impacts of this controller on distribution system were also carefully investigated. Finally, a preliminary measurement and verification approach was also developed.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Deep Learning-Based Adaptive Remedial Action Scheme with Security Margin for Renewable-Dominated Power Grids

The Remedial Action Scheme (RAS) is designed to take corrective actions after detecting predetermined conditions to maintain system transient stability in large interconnected power grids. However, since RAS is usually designed based on a few selected typical operating conditions, it is not optimal in operating conditions that are not considered in the offline design, especially under frequently and dramatically varying operating conditions due to the increasing integration of intermittent renewables. The deep learning-based RAS is proposed to enhance the adaptivity of RAS to varying operating conditions. During the training, a customized loss function is developed to penalize the negative loss and suggest corrective actions with a security margin to avoid triggering under-frequency and over-frequency relays. Simulation results of the reduced United States Western Interconnection system model demonstrate that the proposed deep learning–based RAS can provide optimal corrective actions for unseen operating conditions while maintaining a sufficient security margin.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Proactive Frequency Stability Scheme: A Distributed Framework Based on Particle Filters and Synchrophasors

The reactive nature of traditional under-frequency load shedding schemes can lead to delayed response and unnecessary loss of load. This work presents a proactive framework for power system frequency stability. Bayesian filters and synchrophasors are leveraged to produce predictions after disturbances are detected. By being able to estimate the future state of frequency corrective actions can be taken before the system reaches a critical condition. This proactive approach makes it possible to optimize the response to a disturbance, which results in a decrease in the amount of compensation utilized. The framework is tested via Matlab simulations based on Kundur’s Two-Area System, and the IEEE 14-Bus System. Performance metrics are provided and evaluated against other contemporary solutions found in literature. During testing this framework outperformed other solutions by drastically reducing the amount of load dropped during compensation.

42 ENGINEERING↗

Investigating Power System Primary and Secondary Reserve Interaction under High Wind Penetration Using Frequency Response Model

Power system frequency needs to be maintained close to its nominal value at all times to avoid machine damage, under-frequency load-shedding and even blackouts. Adequate primary frequency response and secondary frequency response are the primary forces to correct an energy imbalance at the second to minute level. As wind energy becomes a larger portion of the world's energy portfolio, there are greater oppotunities for wind to provide frequency response services. This paper addresses one area of frequency control that has been missing in previous work - the reliabilty impacts and interactions between primary and secondary frequency control. The lack of a commercially available tools to simulate the interaction of these two responses has limited the energy industry's understanding of when the depletion of primary control reserve will impact the performance of secondary conrol response or vice versa. To investigate this issue, in this paper we develop a multi-area frequency response integration model with combined primary and secondary frequency control capabilities.

17 WIND ENERGY↗